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-Převod html dokumentace do md formátu (version 0.52) - BETA
+Převod html dokumentace do md formátu (version 0.53) - BETA
===========================================================
VĂ˝sledkem projektu by mÄ›l bĂ˝t skript pro staĹľenĂ a pĹ™evod stávajĂcĂ dokumentace na docs.it4i.cz do md formátu
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-Outgoing connections
-====================
-
-
-
-Connection restrictions
------------------------
-
-Outgoing connections, from Anselm Cluster login nodes to the outside
-world, are restricted to following ports:
-
- |Port|Protocol|
- |---|---|
- |22|ssh|
- |80|http|
- |443|https|
- |9418|git|
-
-Please use **ssh port forwarding** and proxy servers to connect from
-Anselm to all other remote ports.
-
-Outgoing connections, from Anselm Cluster compute nodes are restricted
-to the internal network. Direct connections form compute nodes to
-outside world are cut.
-
-Port forwarding
----------------
-
-### Port forwarding from login nodes
-
-Port forwarding allows an application running on Anselm to connect to
-arbitrary remote host and port.
-
-It works by tunneling the connection from Anselm back to users
-workstation and forwarding from the workstation to the remote host.
-
-Pick some unused port on Anselm login node (for example 6000) and
-establish the port forwarding:
-
-`
-local $ ssh -R 6000:remote.host.com:1234 anselm.it4i.cz
-`
-
-In this example, we establish port forwarding between port 6000 on
-Anselm and port 1234 on the remote.host.com. By accessing
-localhost:6000 on Anselm, an application will see response of
-remote.host.com:1234. The traffic will run via users local workstation.
-
-Port forwarding may be done **using PuTTY** as well. On the PuTTY
-Configuration screen, load your Anselm configuration first. Then go to
-Connection->SSH->Tunnels to set up the port forwarding. Click
-Remote radio button. Insert 6000 to Source port textbox. Insert
-remote.host.com:1234. Click Add button, then Open.
-
-Port forwarding may be established directly to the remote host. However,
-this requires that user has ssh access to remote.host.com
-
-`
-$ ssh -L 6000:localhost:1234 remote.host.com
-`
-
-Note: Port number 6000 is chosen as an example only. Pick any free port.
-
-### Port forwarding from compute nodes
-
-Remote port forwarding from compute nodes allows applications running on
-the compute nodes to access hosts outside Anselm Cluster.
-
-First, establish the remote port forwarding form the login node, as
-[described
-above](outgoing-connections.html#port-forwarding-from-login-nodes).
-
-Second, invoke port forwarding from the compute node to the login node.
-Insert following line into your jobscript or interactive shell
-
-`
-$ ssh -TN -f -L 6000:localhost:6000 login1
-`
-
-In this example, we assume that port forwarding from login1:6000 to
-remote.host.com:1234 has been established beforehand. By accessing
-localhost:6000, an application running on a compute node will see
-response of remote.host.com:1234
-
-### Using proxy servers
-
-Port forwarding is static, each single port is mapped to a particular
-port on remote host. Connection to other remote host, requires new
-forward.
-
-Applications with inbuilt proxy support, experience unlimited access to
-remote hosts, via single proxy server.
-
-To establish local proxy server on your workstation, install and run
-SOCKS proxy server software. On Linux, sshd demon provides the
-functionality. To establish SOCKS proxy server listening on port 1080
-run:
-
-`
-local $ ssh -D 1080 localhost
-`
-
-On Windows, install and run the free, open source [Sock
-Puppet](http://sockspuppet.com/) server.
-
-Once the proxy server is running, establish ssh port forwarding from
-Anselm to the proxy server, port 1080, exactly as [described
-above](outgoing-connections.html#port-forwarding-from-login-nodes).
-
-`
-local $ ssh -R 6000:localhost:1080 anselm.it4i.cz
-`
-
-Now, configure the applications proxy settings to **localhost:6000**.
-Use port forwarding to access the [proxy server from compute
-nodes](outgoing-connections.html#port-forwarding-from-compute-nodes)
-as well .
-
diff --git a/converted/docs.it4i.cz/anselm-cluster-documentation/accessing-the-cluster/outgoing-connections.pdf b/converted/docs.it4i.cz/anselm-cluster-documentation/accessing-the-cluster/outgoing-connections.pdf
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-Shell access and data transfer
-==============================
-
-
-
-Interactive Login
------------------
-
-The Anselm cluster is accessed by SSH protocol via login nodes login1
-and login2 at address anselm.it4i.cz. The login nodes may be addressed
-specifically, by prepending the login node name to the address.
-
- Login address |Port|Protocol| Login node
- ----------------------- |---|---| ----------------------------------------------
- anselm.it4i.cz |22|ssh| round-robin DNS record for login1 and login2
- login1.anselm.it4i.cz |22|ssh| login1
- login2.anselm.it4i.cz |22|ssh| login2
-
-The authentication is by the [private
-key](../../../get-started-with-it4innovations/accessing-the-clusters/shell-access-and-data-transfer/ssh-keys.html)
-
-Please verify SSH fingerprints during the first logon. They are
-identical on all login nodes:
-29:b3:f4:64:b0:73:f5:6f:a7:85:0f:e0:0d:be:76:bf (DSA)
-d4:6f:5c:18:f4:3f:70:ef:bc:fc:cc:2b:fd:13:36:b7Â (RSA)
-
-Â
-
-Private key (`id_rsa/id_rsa.ppk` ): `600 (-rw-------)`s authentication:
-
-On **Linux** or **Mac**, use
-
-`
-local $ ssh -i /path/to/id_rsa username@anselm.it4i.cz
-`
-
-If you see warning message "UNPROTECTED PRIVATE KEY FILE!", use this
-command to set lower permissions to private key file.
-
-`
-local $ chmod 600 /path/to/id_rsa
-`
-
-On **Windows**, use [PuTTY ssh
-client](../../../get-started-with-it4innovations/accessing-the-clusters/shell-access-and-data-transfer/putty/putty.html).
-
-After logging in, you will see the command prompt:
-
- _
- / | |
- / _ __ ___ ___| |_ __ ___
- / / | '_ / __|/ _ | '_ ` _
- / ____ | | | __ __/ | | | | | |
- /_/ __| |_|___/___|_|_| |_| |_|
-
- Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â http://www.it4i.cz/?lang=en
-
- Last login: Tue Jul 9 15:57:38 2013 from your-host.example.com
- [username@login2.anselm ~]$
-
-The environment is **not** shared between login nodes, except for
-[shared filesystems](../storage-1.html#section-1).
-
-Data Transfer
--------------
-
-Data in and out of the system may be transferred by the
-[scp](http://en.wikipedia.org/wiki/Secure_copy) and sftp
-protocols. (Not available yet.) In case large
-volumes of data are transferred, use dedicated data mover node
-dm1.anselm.it4i.cz for increased performance.
-
- |Address|Port|Protocol|
- -------------------- |---|---|- -----------------------------------------
- |anselm.it4i.cz|22|scp, sftp|
- |login1.anselm.it4i.cz|22|scp, sftp|
- |login2.anselm.it4i.cz|22|scp, sftp|
- |dm1.anselm.it4i.cz|22|scp, sftp|
-
-Â The authentication is by the [private
-key](../../../get-started-with-it4innovations/accessing-the-clusters/shell-access-and-data-transfer/ssh-keys.html)
-
-Data transfer rates up to **160MB/s** can be achieved with scp or sftp.
- may be transferred in 1:50h.
-
-To achieve 160MB/s transfer rates, the end user must be connected by 10G
-line all the way to IT4Innovations and use computer with fast processor
-for the transfer. Using Gigabit ethernet connection, up to 110MB/s may
-be expected. Fast cipher (aes128-ctr) should be used.
-
-If you experience degraded data transfer performance, consult your local
-network provider.
-
-On linux or Mac, use scp or sftp client to transfer the data to Anselm:
-
-`
-local $ scp -i /path/to/id_rsa my-local-file username@anselm.it4i.cz:directory/file
-`
-
-`
-local $ scp -i /path/to/id_rsa -r my-local-dir username@anselm.it4i.cz:directory
-`
-
- or
-
-`
-local $ sftp -o IdentityFile=/path/to/id_rsa username@anselm.it4i.cz
-`
-
-Very convenient way to transfer files in and out of the Anselm computer
-is via the fuse filesystem
-[sshfs](http://linux.die.net/man/1/sshfs)
-
-`
-local $ sshfs -o IdentityFile=/path/to/id_rsa username@anselm.it4i.cz:. mountpoint
-`
-
-Using sshfs, the users Anselm home directory will be mounted on your
-local computer, just like an external disk.
-
-Learn more on ssh, scp and sshfs by reading the manpages
-
-`
-$ man ssh
-$ man scp
-$ man sshfs
-`
-
-On Windows, use [WinSCP
-client](http://winscp.net/eng/download.php) to transfer
-the data. The [win-sshfs
-client](http://code.google.com/p/win-sshfs/) provides a
-way to mount the Anselm filesystems directly as an external disc.
-
-More information about the shared file systems is available
-[here](../../storage.html).
-
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-VPN Access
-==========
-
-
-
-Accessing IT4Innovations internal resources via VPN
----------------------------------------------------
-
-**Failed to initialize connection subsystem Win 8.1 - 02-10-15 MS
-patch**
-Workaround can be found at
-[https://docs.it4i.cz/vpn-connection-fail-in-win-8.1](../../vpn-connection-fail-in-win-8.1.html)
-
-Â
-
-For using resources and licenses which are located at IT4Innovations
-local network, it is necessary to VPN connect to this network.
-We use Cisco AnyConnect Secure Mobility Client, which is supported on
-the following operating systems:
-
-- >Windows XP
-- >Windows Vista
-- >Windows 7
-- >Windows 8
-- >Linux
-- >MacOS
-
-It is impossible to connect to VPN from other operating systems.
-
-VPN client installation
-------------------------------------
-
-You can install VPN client from web interface after successful login
-with LDAP credentials on address <https://vpn1.it4i.cz/anselm>
-
-
-
-According to the Java settings after login, the client either
-automatically installs, or downloads installation file for your
-operating system. It is necessary to allow start of installation tool
-for automatic installation.
-
-
-
-access](../executionaccess.jpg/@@images/4d6e7cb7-9aa7-419c-9583-6dfd92b2c015.jpeg "Execution access")
-access
-
-
-After successful installation, VPN connection will be established and
-you can use available resources from IT4I network.
-
-
-
-If your Java setting doesn't allow automatic installation, you can
-download installation file and install VPN client manually.
-
-
-
-After you click on the link, download of installation file will start.
-
-
-
-After successful download of installation file, you have to execute this
-tool with administrator's rights and install VPN client manually.
-
-Working with VPN client
------------------------
-
-You can use graphical user interface or command line interface to run
-VPN client on all supported operating systems. We suggest using GUI.
-
-Before the first login to VPN, you have to fill
-URLÂ **https://vpn1.it4i.cz/anselm** into the text field.
-
-
-
-After you click on the Connect button, you must fill your login
-credentials.
-
-
-
-After a successful login, the client will minimize to the system tray.
-If everything works, you can see a lock in the Cisco tray icon.
-
-
-
-If you right-click on this icon, you will see a context menu in which
-you can control the VPN connection.
-
-
-
-When you connect to the VPN for the first time, the client downloads the
-profile and creates a new item "ANSELM" in the connection list. For
-subsequent connections, it is not necessary to re-enter the URL address,
-but just select the corresponding item.
-
-
-
-Then AnyConnect automatically proceeds like in the case of first logon.
-
-
-
-After a successful logon, you can see a green circle with a tick mark on
-the lock icon.
-
-
-
-For disconnecting, right-click on the AnyConnect client icon in the
-system tray and select **VPN Disconnect**.
-
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-Compute Nodes
-=============
-
-
-
-Nodes Configuration
--------------------
-
-Anselm is cluster of x86-64 Intel based nodes built on Bull Extreme
-Computing bullx technology. The cluster contains four types of compute
-nodes.
-
-###Compute Nodes Without Accelerator
-
--
-
- 180 nodes
-
-
-
--
-
- 2880 cores in total
-
-
-
--
-
- two Intel Sandy Bridge E5-2665, 8-core, 2.4GHz processors per node
-
-
-
--
-
- 64 GB of physical memory per node
-
-
-
-- one 500GB SATA 2,5” 7,2 krpm HDD per node
--
-
- bullx B510 blade servers
-
-
-
--
-
- cn[1-180]
-
-
-
-###Compute Nodes With GPU Accelerator
-
--
-
- 23 nodes
-
-
-
--
-
- 368 cores in total
-
-
-
--
-
- two Intel Sandy Bridge E5-2470, 8-core, 2.3GHz processors per node
-
-
-
--
-
- 96 GB of physical memory per node
-
-
-
-- one 500GB SATA 2,5” 7,2 krpm HDD per node
--
-
- GPU accelerator 1x NVIDIA Tesla Kepler K20 per node
-
-
-
--
-
- bullx B515 blade servers
-
-
-
--
-
- cn[181-203]
-
-
-
-###Compute Nodes With MIC Accelerator
-
--
-
- 4 nodes
-
-
-
--
-
- 64 cores in total
-
-
-
--
-
- two Intel Sandy Bridge E5-2470, 8-core, 2.3GHz processors per node
-
-
-
--
-
- 96 GB of physical memory per node
-
-
-
-- one 500GB SATA 2,5” 7,2 krpm HDD per node
--
-
- MIC accelerator 1x Intel Phi 5110P per node
-
-
-
--
-
- bullx B515 blade servers
-
-
-
--
-
- cn[204-207]
-
-
-
-###Fat Compute Nodes
-
--
-
- 2 nodes
-
-
-
--
-
- 32 cores in total
-
-
-
--
-
- 2 Intel Sandy Bridge E5-2665, 8-core, 2.4GHz processors per node
-
-
-
--
-
- 512 GB of physical memory per node
-
-
-
-- two 300GB SAS 3,5”15krpm HDD (RAID1) per node
--
-
- two 100GB SLC SSD per node
-
-
-
--
-
- bullx R423-E3 servers
-
-
-
--
-
- cn[208-209]
-
-
-
-Â
-
-
-
-**Figure Anselm bullx B510 servers**
-
-### Compute Nodes Summary
-
- |Node type|Count|Range|Memory|Cores|[Access](resource-allocation-and-job-execution/resources-allocation-policy.html)|
- |---|---|---|---|---|---|
- |Nodes without accelerator|180|cn[1-180]|64GB|16 @ 2.4Ghz|qexp, qprod, qlong, qfree|
- |Nodes with GPU accelerator|23|cn[181-203]|96GB|16 @ 2.3Ghz|qgpu, qprod|
- |Nodes with MIC accelerator|4|cn[204-207]|96GB|16 @ 2.3GHz|qmic, qprod|
- |Fat compute nodes|2|cn[208-209]|512GB|16 @ 2.4GHz|qfat, qprod|
-
-Processor Architecture
-----------------------
-
-Anselm is equipped with Intel Sandy Bridge processors Intel Xeon E5-2665
-(nodes without accelerator and fat nodes) and Intel Xeon E5-2470 (nodes
-with accelerator). Processors support Advanced Vector Extensions (AVX)
-256-bit instruction set.
-
-### Intel Sandy Bridge E5-2665 Processor
-
-- eight-core
-- speed: 2.4 GHz, up to 3.1 GHz using Turbo Boost Technology
-- peak performance: 19.2 Gflop/s per
- core
-- caches:
-
-
- - L2: 256 KB per core
- - L3: 20 MB per processor
-
-
-
-- memory bandwidth at the level of the processor: 51.2 GB/s
-
-### Intel Sandy Bridge E5-2470 Processor
-
-- eight-core
-- speed: 2.3 GHz, up to 3.1 GHz using Turbo Boost Technology
-- peak performance: 18.4 Gflop/s per
- core
-- caches:
-
-
- - L2: 256 KB per core
- - L3: 20 MB per processor
-
-
-
-- memory bandwidth at the level of the processor: 38.4 GB/s
-
-Â
-
-Nodes equipped with Intel Xeon E5-2665 CPU have set PBS resource
-attribute cpu_freq = 24, nodes equipped with Intel Xeon E5-2470 CPU
-have set PBS resource attribute cpu_freq = 23.
-
-`
-$ qsub -A OPEN-0-0 -q qprod -l select=4:ncpus=16:cpu_freq=24 -I
-`
-
-In this example, we allocate 4 nodes, 16 cores at 2.4GHhz per node.
-
-Intel Turbo Boost Technology is used by default, you can disable it for
-all nodes of job by using resource attribute cpu_turbo_boost.
-
- $ qsub -A OPEN-0-0 -q qprod -l select=4:ncpus=16 -l cpu_turbo_boost=0 -I
-
-Memory Architecture
--------------------
-
-### Compute Node Without Accelerator
-
-- 2 sockets
-- Memory Controllers are integrated into processors.
-
-
- - 8 DDR3 DIMMS per node
- - 4 DDR3 DIMMS per CPU
- - 1 DDR3 DIMMS per channel
- - Data rate support: up to 1600MT/s
-
-
-
-- Populated memory: 8x 8GB DDR3 DIMM 1600Mhz
-
-### Compute Node With GPU or MIC Accelerator
-
-- 2 sockets
-- Memory Controllers are integrated into processors.
-
-
- - 6 DDR3 DIMMS per node
- - 3 DDR3 DIMMS per CPU
- - 1 DDR3 DIMMS per channel
- - Data rate support: up to 1600MT/s
-
-
-
-- Populated memory: 6x 16GB DDR3 DIMM 1600Mhz
-
-### Fat Compute Node
-
-- 2 sockets
-- Memory Controllers are integrated into processors.
-
-
- - 16 DDR3 DIMMS per node
- - 8 DDR3 DIMMS per CPU
- - 2 DDR3 DIMMS per channel
- - Data rate support: up to 1600MT/s
-
-
-
-- Populated memory: 16x 32GB DDR3 DIMM 1600Mhz
-
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-Environment and Modules
-=======================
-
-
-
-### Environment Customization
-
-After logging in, you may want to configure the environment. Write your
-preferred path definitions, aliases, functions and module loads in the
-.bashrc file
-
-`
-# ./bashrc
-
-# Source global definitions
-if [ -f /etc/bashrc ]; then
- . /etc/bashrc
-fi
-
-# User specific aliases and functions
-alias qs='qstat -a'
-module load PrgEnv-gnu
-
-# Display informations to standard output - only in interactive ssh session
-if [ -n "$SSH_TTY" ]
-then
- module list # Display loaded modules
-fi
-`
-
-Do not run commands outputing to standard output (echo, module list,
-etc) in .bashrc for non-interactive SSH sessions. It breaks fundamental
-functionality (scp, PBS) of your account! Take care for SSH session
-interactivity for such commands as
- stated in the previous example.
-in the previous example.
-
-### Application Modules
-
-In order to configure your shell for running particular application on
-Anselm we use Module package interface.
-
-The modules set up the application paths, library paths and environment
-variables for running particular application.
-
-We have also second modules repository. This modules repository is
-created using tool called EasyBuild. On Salomon cluster, all modules
-will be build by this tool. If you want to use software from this
-modules repository, please follow instructions in section [Application
-Modules
-Path Expansion](environment-and-modules.html#EasyBuild).
-
-The modules may be loaded, unloaded and switched, according to momentary
-needs.
-
-To check available modules use
-
-`
-$ module avail
-`
-
-To load a module, for example the octave module use
-
-`
-$ module load octave
-`
-
-loading the octave module will set up paths and environment variables of
-your active shell such that you are ready to run the octave software
-
-To check loaded modules use
-
-`
-$ module list
-`
-
-Â To unload a module, for example the octave module use
-
-`
-$ module unload octave
-`
-
-Learn more on modules by reading the module man page
-
-`
-$ man module
-`
-
-Following modules set up the development environment
-
-PrgEnv-gnu sets up the GNU development environment in conjunction with
-the bullx MPI library
-
-PrgEnv-intel sets up the INTEL development environment in conjunction
-with the Intel MPI library
-
-### Application Modules Path Expansion
-
-All application modules on Salomon cluster (and further) will be build
-using tool called
-[EasyBuild](http://hpcugent.github.io/easybuild/ "EasyBuild").
-In case that you want to use some applications that are build by
-EasyBuild already, you have to modify your MODULEPATH environment
-variable.
-
-`
-export MODULEPATH=$MODULEPATH:/apps/easybuild/modules/all/
-`
-
-This command expands your searched paths to modules. You can also add
-this command to the .bashrc file to expand paths permanently. After this
-command, you can use same commands to list/add/remove modules as is
-described above.
-
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-Hardware Overview
-=================
-
-
-
-The Anselm cluster consists of 209 computational nodes named cn[1-209]
-of which 180 are regular compute nodes, 23 GPU Kepler K20 accelerated
-nodes, 4 MIC Xeon Phi 5110 accelerated nodes and 2 fat nodes. Each node
-is a powerful x86-64 computer,
-equipped with 16 cores (two eight-core Intel Sandy Bridge processors),
-at least 64GB RAM, and local hard drive. The user access to the Anselm
-cluster is provided by two login nodes login[1,2]. The nodes are
-interlinked by high speed InfiniBand and Ethernet networks. All nodes
-share 320TB /home disk storage to store the user files. The 146TB shared
-/scratch storage is available for the scratch data.
-
-The Fat nodes are equipped with large amount (512GB) of memory.
-Virtualization infrastructure provides resources to run long term
-servers and services in virtual mode. Fat nodes and virtual servers may
-access 45 TB of dedicated block storage. Accelerated nodes, fat nodes,
-and virtualization infrastructure are available [upon
-request](https://support.it4i.cz/rt) made by a PI.
-
-Schematic representation of the Anselm cluster. Each box represents a
-node (computer) or storage capacity:
-
-User-oriented infrastructure
-Storage
-Management infrastructure
- --------
- login1
- login2
- dm1
- --------
-
-Rack 01, Switch isw5
-
- -------- |---|---|---- -------------- -------------- --------------
- cn186 cn187 cn188 cn189
- cn181 cn182 cn183 cn184 cn185
- -------- |---|---|---- -------------- -------------- --------------
-
-Rack 01, Switch isw4
-
-cn29
-cn30
-cn31
-cn32
-cn33
-cn34
-cn35
-cn36
-cn19
-cn20
-cn21
-cn22
-cn23
-cn24
-cn25
-cn26
-cn27
-cn28
-<col width="100%" />
- |Â <p>Â <p>Lustre FS<p>/home320TB<p>Â <p>Â \ |
- |Lustre FS<p>/scratch146TB\ |
-
-Management
-nodes
-Block storage
-45 TB
-Virtualization
-infrastructure
-servers
-...
-Srv node
-Srv node
-Srv node
-...
-Rack 01, Switch isw0
-
-cn11
-cn12
-cn13
-cn14
-cn15
-cn16
-cn17
-cn18
-cn1
-cn2
-cn3
-cn4
-cn5
-cn6
-cn7
-cn8
-cn9
-cn10
-Rack 02, Switch isw10
-
-cn73
-cn74
-cn75
-cn76
-cn77
-cn78
-cn79
-cn80
-cn190
-cn191
-cn192
-cn205
-cn206
-Rack 02, Switch isw9
-
-cn65
-cn66
-cn67
-cn68
-cn69
-cn70
-cn71
-cn72
-cn55
-cn56
-cn57
-cn58
-cn59
-cn60
-cn61
-cn62
-cn63
-cn64
-Rack 02, Switch isw6
-
-cn47
-cn48
-cn49
-cn50
-cn51
-cn52
-cn53
-cn54
-cn37
-cn38
-cn39
-cn40
-cn41
-cn42
-cn43
-cn44
-cn45
-cn46
-Rack 03, Switch isw15
-
-cn193
-cn194
-cn195
-cn207
-cn117
-cn118
-cn119
-cn120
-cn121
-cn122
-cn123
-cn124
-cn125
-cn126
-Rack 03, Switch isw14
-
-cn109
-cn110
-cn111
-cn112
-cn113
-cn114
-cn115
-cn116
-cn99
-cn100
-cn101
-cn102
-cn103
-cn104
-cn105
-cn106
-cn107
-cn108
-Rack 03, Switch isw11
-
-cn91
-cn92
-cn93
-cn94
-cn95
-cn96
-cn97
-cn98
-cn81
-cn82
-cn83
-cn84
-cn85
-cn86
-cn87
-cn88
-cn89
-cn90
-Rack 04, Switch isw20
-
-cn173
-cn174
-cn175
-cn176
-cn177
-cn178
-cn179
-cn180
-cn163
-cn164
-cn165
-cn166
-cn167
-cn168
-cn169
-cn170
-cn171
-cn172
-Rack 04, **Switch** isw19
-
-cn155
-cn156
-cn157
-cn158
-cn159
-cn160
-cn161
-cn162
-cn145
-cn146
-cn147
-cn148
-cn149
-cn150
-cn151
-cn152
-cn153
-cn154
-Rack 04, Switch isw16
-
-cn137
-cn138
-cn139
-cn140
-cn141
-cn142
-cn143
-cn144
-cn127
-cn128
-cn129
-cn130
-cn131
-cn132
-cn133
-cn134
-cn135
-cn136
-Rack 05, Switch isw21
-
- -------- |---|---|---- -------------- -------------- --------------
- cn201 cn202 cn203 cn204
- cn196 cn197 cn198 cn199 cn200
- -------- |---|---|---- -------------- -------------- --------------
-
- ----------------
- Fat node cn208
- Fat node cn209
- ...
- ----------------
-
-The cluster compute nodes cn[1-207] are organized within 13 chassis.Â
-
-There are four types of compute nodes:
-
-- 180 compute nodes without the accelerator
-- 23 compute nodes with GPU accelerator - equipped with NVIDIA Tesla
- Kepler K20
-- 4Â compute nodes with MIC accelerator - equipped with Intel Xeon Phi
- 5110P
-- 2 fat nodes - equipped with 512GB RAM and two 100GB SSD drives
-
-[More about Compute nodes](compute-nodes.html).
-
-GPU and accelerated nodes are available upon request, see the [Resources
-Allocation
-Policy](resource-allocation-and-job-execution/resources-allocation-policy.html).
-
-All these nodes are interconnected by fast
-InfiniBand class="WYSIWYG_LINK">QDR
-network and Ethernet network. [More about the
-Network](network.html).
-Every chassis provides Infiniband switch, marked **isw**, connecting all
-nodes in the chassis, as well as connecting the chassis to the upper
-level switches.
-
-All nodes share 360TB /home disk storage to store user files. The 146TB
-shared /scratch storage is available for the scratch data. These file
-systems are provided by Lustre parallel file system. There is also local
-disk storage available on all compute nodes /lscratch. [More about
-
-Storage](storage.html).
-
-The user access to the Anselm cluster is provided by two login nodes
-login1, login2, and data mover node dm1. [More about accessing
-cluster.](accessing-the-cluster.html)
-
-Â The parameters are summarized in the following tables:
-
-In general**
-Primary purpose
-High Performance Computing
-Architecture of compute nodes
-x86-64
-Operating system
-Linux
-[**Compute nodes**](compute-nodes.html)
-Totally
-209
-Processor cores
-16 (2x8 cores)
-RAM
-min. 64 GB, min. 4 GB per core
-Local disk drive
-yes - usually 500 GB
-Compute network
-InfiniBand QDR, fully non-blocking, fat-tree
-w/o accelerator
-180, cn[1-180]
-GPU accelerated
-23, cn[181-203]
-MIC accelerated
-4, cn[204-207]
-Fat compute nodes
-2, cn[208-209]
-In total**
-Total theoretical peak performance (Rpeak)
-94 Tflop/s
-Total max. LINPACK performance (Rmax)
-73 Tflop/s
-Total amount of RAM
-15.136 TB
- |Node|Processor|Memory|Accelerator|
- |---|---|---|---|
- |w/o accelerator|2x Intel Sandy Bridge E5-2665, 2.4GHz|64GB|-|
- |GPU accelerated|2x Intel Sandy Bridge E5-2470, 2.3GHz|96GB|NVIDIA Kepler K20|
- |MIC accelerated|2x Intel Sandy Bridge E5-2470, 2.3GHz|96GB|Intel Xeon Phi P5110|
- |Fat compute node|2x Intel Sandy Bridge E5-2665, 2.4GHz|512GB|-|
-
-Â For more details please refer to the [Compute
-nodes](compute-nodes.html),
-[Storage](storage.html), and
-[Network](network.html).
-
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-Introduction
-============
-
-
-
-Welcome to Anselm supercomputer cluster. The Anselm cluster consists of
-209 compute nodes, totaling 3344 compute cores with 15TB RAM and giving
-over 94 Tflop/s theoretical peak performance. Each node is a
-powerful x86-64 computer, equipped with 16
-cores, at least 64GB RAM, and 500GB harddrive. Nodes are interconnected
-by fully non-blocking fat-tree Infiniband network and equipped with
-Intel Sandy Bridge processors. A few nodes are also equipped with NVIDIA
-Kepler GPU or Intel Xeon Phi MIC accelerators. Read more in [Hardware
-Overview](hardware-overview.html).
-
-The cluster runs bullx Linux [
-](http://www.bull.com/bullx-logiciels/systeme-exploitation.html)[operating
-system](software/operating-system.html), which is
-compatible with the RedHat [
-Linux
-family.](http://upload.wikimedia.org/wikipedia/commons/1/1b/Linux_Distribution_Timeline.svg)
-We have installed a wide range of
-[software](software.1.html) packages targeted at
-different scientific domains. These packages are accessible via the
-[modules environment](environment-and-modules.html).
-
-User data shared file-system (HOME, 320TB) and job data shared
-file-system (SCRATCH, 146TB) are available to users.
-
-The PBS Professional workload manager provides [computing resources
-allocations and job
-execution](resource-allocation-and-job-execution.html).
-
-Read more on how to [apply for
-resources](../get-started-with-it4innovations/applying-for-resources.html),
-[obtain login
-credentials,](../get-started-with-it4innovations/obtaining-login-credentials.html)
-and [access the cluster](accessing-the-cluster.html).
-
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-Network
-=======
-
-
-
-All compute and login nodes of Anselm are interconnected by
-[Infiniband](http://en.wikipedia.org/wiki/InfiniBand)
-QDR network and by Gigabit
-[Ethernet](http://en.wikipedia.org/wiki/Ethernet)
-network. Both networks may be used to transfer user data.
-
-Infiniband Network
-------------------
-
-All compute and login nodes of Anselm are interconnected by a
-high-bandwidth, low-latency
-[Infiniband](http://en.wikipedia.org/wiki/InfiniBand)
-QDR network (IB 4x QDR, 40 Gbps). The network topology is a fully
-non-blocking fat-tree.
-
-The compute nodes may be accessed via the Infiniband network using ib0
-network interface, in address range 10.2.1.1-209. The MPI may be used to
-establish native Infiniband connection among the nodes.
-
-The network provides **2170MB/s** transfer rates via the TCP connection
-(single stream) and up to **3600MB/s** via native Infiniband protocol.
-
-The Fat tree topology ensures that peak transfer rates are achieved
-between any two nodes, independent of network traffic exchanged among
-other nodes concurrently.
-
-Ethernet Network
-----------------
-
-The compute nodes may be accessed via the regular Gigabit Ethernet
-network interface eth0, in address range 10.1.1.1-209, or by using
-aliases cn1-cn209.
-The network provides **114MB/s** transfer rates via the TCP connection.
-
-Example
--------
-
-`
-$ qsub -q qexp -l select=4:ncpus=16 -N Name0 ./myjob
-$ qstat -n -u username
- Req'd Req'd Elap
-Job ID Username Queue Jobname SessID NDS TSK Memory Time S Time
---------------- -------- -- |---|---| ------ --- --- ------ ----- - -----
-15209.srv11 username qexp Name0 5530 4 64 -- 01:00 R 00:00
- cn17/0*16+cn108/0*16+cn109/0*16+cn110/0*16
-
-$ ssh 10.2.1.110
-$ ssh 10.1.1.108
-`
-
-In this example, we access the node cn110 by Infiniband network via the
-ib0 interface, then from cn110 to cn108 by Ethernet network.
-
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-PRACE User Support
-==================
-
-
-
-Intro
------
-
-PRACE users coming to Anselm as to TIER-1 system offered through the
-DECI calls are in general treated as standard users and so most of the
-general documentation applies to them as well. This section shows the
-main differences for quicker orientation, but often uses references to
-the original documentation. PRACE users who don't undergo the full
-procedure (including signing the IT4I AuP on top of the PRACE AuP) will
-not have a password and thus access to some services intended for
-regular users. This can lower their comfort, but otherwise they should
-be able to use the TIER-1 system as intended. Please see the [Obtaining
-Login Credentials
-section](../get-started-with-it4innovations/obtaining-login-credentials/obtaining-login-credentials.html),
-if the same level of access is required.
-
-All general [PRACE User
-Documentation](http://www.prace-ri.eu/user-documentation/)
-should be read before continuing reading the local documentation here.
-
-Help and Support
---------------------
-
-If you have any troubles, need information, request support or want to
-install additional software, please use [PRACE
-Helpdesk](http://www.prace-ri.eu/helpdesk-guide264/).
-
-Information about the local services are provided in the [introduction
-of general user documentation](introduction.html).
-Please keep in mind, that standard PRACE accounts don't have a password
-to access the web interface of the local (IT4Innovations) request
-tracker and thus a new ticket should be created by sending an e-mail to
-support[at]it4i.cz.
-
-Obtaining Login Credentials
----------------------------
-
-In general PRACE users already have a PRACE account setup through their
-HOMESITE (institution from their country) as a result of rewarded PRACE
-project proposal. This includes signed PRACE AuP, generated and
-registered certificates, etc.
-
-If there's a special need a PRACE user can get a standard (local)
-account at IT4Innovations. To get an account on the Anselm cluster, the
-user needs to obtain the login credentials. The procedure is the same as
-for general users of the cluster, so please see the corresponding
-[section of the general documentation
-here](../get-started-with-it4innovations/obtaining-login-credentials.html).
-
-Accessing the cluster
----------------------
-
-### Access with GSI-SSH
-
-For all PRACE users the method for interactive access (login) and data
-transfer based on grid services from Globus Toolkit (GSI SSH and
-GridFTP) is supported.
-
-The user will need a valid certificate and to be present in the PRACE
-LDAP (please contact your HOME SITE or the primary investigator of your
-project for LDAP account creation).
-
-Most of the information needed by PRACE users accessing the Anselm
-TIER-1 system can be found here:
-
-- [General user's
- FAQ](http://www.prace-ri.eu/Users-General-FAQs)
-- [Certificates
- FAQ](http://www.prace-ri.eu/Certificates-FAQ)
-- [Interactive access using
- GSISSH](http://www.prace-ri.eu/Interactive-Access-Using-gsissh)
-- [Data transfer with
- GridFTP](http://www.prace-ri.eu/Data-Transfer-with-GridFTP-Details)
-- [Data transfer with
- gtransfer](http://www.prace-ri.eu/Data-Transfer-with-gtransfer)
-
-Â
-
-Before you start to use any of the services don't forget to create a
-proxy certificate from your certificate:
-
- $ grid-proxy-init
-
-To check whether your proxy certificate is still valid (by default it's
-valid 12 hours), use:
-
- $ grid-proxy-info
-
-Â
-
-To access Anselm cluster, two login nodes running GSI SSH service are
-available. The service is available from public Internet as well as from
-the internal PRACE network (accessible only from other PRACE partners).
-
-***Access from PRACE network:**
-
-It is recommended to use the single DNS name
-anselm-prace.it4i.cz which is distributed
-between the two login nodes. If needed, user can login directly to one
-of the login nodes. The addresses are:
-
- Login address |Port|Protocol| Login node
- ----------------------------- |---|---| ------------------
- anselm-prace.it4i.cz 2222 gsissh login1 or login2
- login1-prace.anselm.it4i.cz 2222 gsissh login1
- login2-prace.anselm.it4i.cz 2222 gsissh login2
-
-Â
-
- $ gsissh -p 2222 anselm-prace.it4i.cz
-
-When logging from other PRACE system, the prace_service script can be
-used:
-
- $ gsissh `prace_service -i -s anselm`
-
-Â
-
-***Access from public Internet:**
-
-It is recommended to use the single DNS name
-anselm.it4i.cz which is distributed between the
-two login nodes. If needed, user can login directly to one of the login
-nodes. The addresses are:
-
- Login address |Port|Protocol| Login node
- ----------------------- |---|---| ------------------
- anselm.it4i.cz 2222 gsissh login1 or login2
- login1.anselm.it4i.cz 2222 gsissh login1
- login2.anselm.it4i.cz 2222 gsissh login2
-
- $ gsissh -p 2222 anselm.it4i.cz
-
-When logging from other PRACE system, the
-prace_service script can be used:
-
- $ gsissh `prace_service -e -s anselm`
-
-Â
-
-Although the preferred and recommended file transfer mechanism is [using
-GridFTP](prace.html#file-transfers), the GSI SSH
-implementation on Anselm supports also SCP, so for small files transfer
-gsiscp can be used:
-
- $ gsiscp -P 2222 _LOCAL_PATH_TO_YOUR_FILE_ anselm.it4i.cz:_ANSELM_PATH_TO_YOUR_FILE_
-
- $ gsiscp -P 2222 anselm.it4i.cz:_ANSELM_PATH_TO_YOUR_FILE_ _LOCAL_PATH_TO_YOUR_FILE_
-
- $ gsiscp -P 2222 _LOCAL_PATH_TO_YOUR_FILE_ anselm-prace.it4i.cz:_ANSELM_PATH_TO_YOUR_FILE_
-
- $ gsiscp -P 2222 anselm-prace.it4i.cz:_ANSELM_PATH_TO_YOUR_FILE_ _LOCAL_PATH_TO_YOUR_FILE_
-
-### Access to X11 applications (VNC)
-
-If the user needs to run X11 based graphical application and does not
-have a X11 server, the applications can be run using VNC service. If the
-user is using regular SSH based access, please see the [section in
-general
-documentation](https://docs.it4i.cz/anselm-cluster-documentation/resolveuid/11e53ad0d2fd4c5187537f4baeedff33).
-
-If the user uses GSI SSH based access, then the procedure is similar to
-the SSH based access ([look
-here](https://docs.it4i.cz/anselm-cluster-documentation/resolveuid/11e53ad0d2fd4c5187537f4baeedff33)),
-only the port forwarding must be done using GSI SSH:
-
- $ gsissh -p 2222 anselm.it4i.cz -L 5961:localhost:5961
-
-### Access with SSH
-
-After successful obtainment of login credentials for the local
-IT4Innovations account, the PRACE users can access the cluster as
-regular users using SSH. For more information please see the [section in
-general
-documentation](https://docs.it4i.cz/anselm-cluster-documentation/resolveuid/5d3d6f3d873a42e584cbf4365c4e251b).
-
-File transfers
-------------------
-
-PRACE users can use the same transfer mechanisms as regular users (if
-they've undergone the full registration procedure). For information
-about this, please see [the section in the general
-documentation](https://docs.it4i.cz/anselm-cluster-documentation/resolveuid/5d3d6f3d873a42e584cbf4365c4e251b).
-
-Apart from the standard mechanisms, for PRACE users to transfer data
-to/from Anselm cluster, a GridFTP server running Globus Toolkit GridFTP
-service is available. The service is available from public Internet as
-well as from the internal PRACE network (accessible only from other
-PRACE partners).
-
-There's one control server and three backend servers for striping and/or
-backup in case one of them would fail.
-
-***Access from PRACE network:**
-
- Login address Port Node role
- ----- |---|---|
- gridftp-prace.anselm.it4i.cz 2812 Front end /control server
- login1-prace.anselm.it4i.cz 2813 Backend / data mover server
- login2-prace.anselm.it4i.cz 2813 Backend / data mover server
- dm1-prace.anselm.it4i.cz 2813 Backend / data mover server
-
-Copy files **to** Anselm by running the following commands on your local
-machine:
-
- $ globus-url-copy file://_LOCAL_PATH_TO_YOUR_FILE_ gsiftp://gridftp-prace.anselm.it4i.cz:2812/home/prace/_YOUR_ACCOUNT_ON_ANSELM_/_PATH_TO_YOUR_FILE_
-
-Or by using prace_service script:
-
- $ globus-url-copy file://_LOCAL_PATH_TO_YOUR_FILE_ gsiftp://`prace_service -i -f anselm`/home/prace/_YOUR_ACCOUNT_ON_ANSELM_/_PATH_TO_YOUR_FILE_
-
-Copy files **from** Anselm:
-
- $ globus-url-copy gsiftp://gridftp-prace.anselm.it4i.cz:2812/home/prace/_YOUR_ACCOUNT_ON_ANSELM_/_PATH_TO_YOUR_FILE_ file://_LOCAL_PATH_TO_YOUR_FILE_
-
-Or by using prace_service script:
-
- $ globus-url-copy gsiftp://`prace_service -i -f anselm`/home/prace/_YOUR_ACCOUNT_ON_ANSELM_/_PATH_TO_YOUR_FILE_ file://_LOCAL_PATH_TO_YOUR_FILE_
-
-Â
-
-***Access from public Internet:**
-
- Login address Port Node role
- ------------------------ |---|---|-------------------
- gridftp.anselm.it4i.cz 2812 Front end /control server
- login1.anselm.it4i.cz 2813 Backend / data mover server
- login2.anselm.it4i.cz 2813 Backend / data mover server
- dm1.anselm.it4i.cz 2813 Backend / data mover server
-
-Copy files **to** Anselm by running the following commands on your local
-machine:
-
- $ globus-url-copy file://_LOCAL_PATH_TO_YOUR_FILE_ gsiftp://gridftp.anselm.it4i.cz:2812/home/prace/_YOUR_ACCOUNT_ON_ANSELM_/_PATH_TO_YOUR_FILE_
-
-Or by using prace_service script:
-
- $ globus-url-copy file://_LOCAL_PATH_TO_YOUR_FILE_ gsiftp://`prace_service -e -f anselm`/home/prace/_YOUR_ACCOUNT_ON_ANSELM_/_PATH_TO_YOUR_FILE_
-
-Copy files **from** Anselm:
-
- $ globus-url-copy gsiftp://gridftp.anselm.it4i.cz:2812/home/prace/_YOUR_ACCOUNT_ON_ANSELM_/_PATH_TO_YOUR_FILE_ file://_LOCAL_PATH_TO_YOUR_FILE_
-
-Or by using prace_service script:
-
- $ globus-url-copy gsiftp://`prace_service -e -f anselm`/home/prace/_YOUR_ACCOUNT_ON_ANSELM_/_PATH_TO_YOUR_FILE_ file://_LOCAL_PATH_TO_YOUR_FILE_
-
-Â
-
-Generally both shared file systems are available through GridFTP:
-
- |File system mount point|Filesystem|Comment|
- |---|---|
- |/home|Lustre|Default HOME directories of users in format /home/prace/login/|
- |/scratch|Lustre|Shared SCRATCH mounted on the whole cluster|
-
-More information about the shared file systems is available
-[here](storage.html).
-
-Usage of the cluster
---------------------
-
-There are some limitations for PRACE user when using the cluster. By
-default PRACE users aren't allowed to access special queues in the PBS
-Pro to have high priority or exclusive access to some special equipment
-like accelerated nodes and high memory (fat) nodes. There may be also
-restrictions obtaining a working license for the commercial software
-installed on the cluster, mostly because of the license agreement or
-because of insufficient amount of licenses.
-
-For production runs always use scratch file systems, either the global
-shared or the local ones. The available file systems are described
-[here](hardware-overview.html).
-
-### Software, Modules and PRACE Common Production Environment
-
-All system wide installed software on the cluster is made available to
-the users via the modules. The information about the environment and
-modules usage is in this [section of general
-documentation](environment-and-modules.html).
-
-PRACE users can use the "prace" module to use the [PRACE Common
-Production
-Environment](http://www.prace-ri.eu/PRACE-common-production).
-
- $ module load prace
-
-Â
-
-### Resource Allocation and Job Execution
-
-General information about the resource allocation, job queuing and job
-execution is in this [section of general
-documentation](resource-allocation-and-job-execution/introduction.html).
-
-For PRACE users, the default production run queue is "qprace". PRACE
-users can also use two other queues "qexp" and "qfree".
-
- -------------------------------------------------------------------------------------------------------------------------
- queue Active project Project resources Nodes priority authorization walltime
-
- --------------------- -|---|---|---|- ---- |---|---|----- -------------
- **qexp** no none required 2 reserved, high no 1 / 1h
- \ 8 total
-
- gt; 0 >1006 nodes, max 86 per job 0 no 24 / 48h> 0 178 w/o accelerator medium no 24 / 48h
- \
-
-
- **qfree** yes none required 178 w/o accelerator very low no 12 / 12h
- \
- -------------------------------------------------------------------------------------------------------------------------
-
-qprace**, the PRACE \***: This queue is intended for
-normal production runs. It is required that active project with nonzero
-remaining resources is specified to enter the qprace. The queue runs
-with medium priority and no special authorization is required to use it.
-The maximum runtime in qprace is 12 hours. If the job needs longer time,
-it must use checkpoint/restart functionality.
-
-### Accounting & Quota
-
-The resources that are currently subject to accounting are the core
-hours. The core hours are accounted on the wall clock basis. The
-accounting runs whenever the computational cores are allocated or
-blocked via the PBS Pro workload manager (the qsub command), regardless
-of whether the cores are actually used for any calculation. See [example
-in the general
-documentation](resource-allocation-and-job-execution/resources-allocation-policy.html).
-
-PRACE users should check their project accounting using the [PRACE
-Accounting Tool
-(DART)](http://www.prace-ri.eu/accounting-report-tool/).
-
-Users who have undergone the full local registration procedure
-(including signing the IT4Innovations Acceptable Use Policy) and who
-have received local password may check at any time, how many core-hours
-have been consumed by themselves and their projects using the command
-"it4ifree". Please note that you need to know your user password to use
-the command and that the displayed core hours are "system core hours"
-which differ from PRACE "standardized core hours".
-
-The **it4ifree** command is a part of it4i.portal.clients package,
-located here:
-<https://pypi.python.org/pypi/it4i.portal.clients>
-
- $ it4ifree
- Password:
-     PID  Total Used ...by me Free
- Â Â -------- ------- ------ -------- -------
- Â Â OPEN-0-0 1500000 400644Â Â 225265 1099356
- Â Â DD-13-1 Â Â 10000 2606 2606 7394
-
-Â
-
-By default file system quota is applied. To check the current status of
-the quota use
-
- $ lfs quota -u USER_LOGIN /home
- $ lfs quota -u USER_LOGIN /scratch
-
-If the quota is insufficient, please contact the
-[support](prace.html#help-and-support) and request an
-increase.
-
-Â
-
-Â
-
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-Remote visualization service
-============================
-
-Introduction
-------------
-
-The goal of this service is to provide the users a GPU accelerated use
-of OpenGL applications, especially for pre- and post- processing work,
-where not only the GPU performance is needed but also fast access to the
-shared file systems of the cluster and a reasonable amount of RAM.
-
-The service is based on integration of open source tools VirtualGL and
-TurboVNC together with the cluster's job scheduler PBS Professional.
-
-Currently two compute nodes are dedicated for this service with
-following configuration for each node:
-
-[**Visualization node
-configuration**](compute-nodes.html)
-CPU
-2x Intel Sandy Bridge E5-2670, 2.6GHz
-Processor cores
-16 (2x8 cores)
-RAM
-64 GB, min. 4 GB per core
-GPU
-NVIDIA Quadro 4000, 2GB RAM
-Local disk drive
-yes - 500 GB
-Compute network
-InfiniBand QDR
-Schematic overview
-------------------
-
-
-
-
-
-How to use the service
-----------------------
-
-### Setup and start your own TurboVNC server.
-
-TurboVNC is designed and implemented for cooperation with VirtualGL and
-available for free for all major platforms. For more information and
-download, please refer to: <http://sourceforge.net/projects/turbovnc/>
-
-Always use TurboVNC on both sides** (server and client) **don't mix
-TurboVNC and other VNC implementations** (TightVNC, TigerVNC, ...) as
-the VNC protocol implementation may slightly differ and diminish your
-user experience by introducing picture artifacts, etc.
-
-The procedure is:
-
-#### 1. Connect to a login node. {#1-connect-to-a-login-node}
-
-Please [follow the
-documentation](https://docs.it4i.cz/anselm-cluster-documentation/resolveuid/5d3d6f3d873a42e584cbf4365c4e251b).
-
-#### 2. Run your own instance of TurboVNC server. {#2-run-your-own-instance-of-turbovnc-server}
-
-To have the OpenGL acceleration, **24 bit color depth must be used**.
-Otherwise only the geometry (desktop size) definition is needed.
-
-*At first VNC server run you need to define a password.*
-
-This example defines desktop with dimensions 1200x700 pixels and 24 bit
-color depth.
-
-`
-$ module load turbovnc/1.2.2
-$ vncserver -geometry 1200x700 -depth 24
-
-Desktop 'TurboVNC: login2:1 (username)' started on display login2:1
-
-Starting applications specified in /home/username/.vnc/xstartup.turbovnc
-Log file is /home/username/.vnc/login2:1.log
-`
-
-#### 3. Remember which display number your VNC server runs (you will need it in the future to stop the server). {#3-remember-which-display-number-your-vnc-server-runs-you-will-need-it-in-the-future-to-stop-the-server}
-
-`
-$ vncserver -list
-
-TurboVNC server sessions:
-
-X DISPLAY # PROCESS ID
-:1 23269
-`
-
-In this example the VNC server runs on display **:1**.
-
-#### 4. Remember the exact login node, where your VNC server runs. {#4-remember-the-exact-login-node-where-your-vnc-server-runs}
-
-`
-$ uname -n
-login2
-`
-
-In this example the VNC server runs on **login2**.
-
-#### 5. Remember on which TCP port your own VNC server is running. {#5-remember-on-which-tcp-port-your-own-vnc-server-is-running}
-
-To get the port you have to look to the log file of your VNC server.
-
-`
-$ grep -E "VNC.*port" /home/username/.vnc/login2:1.log
-20/02/2015 14:46:41 Listening for VNC connections on TCP port 5901
-`
-
-In this example the VNC server listens on TCP port **5901**.
-
-#### 6. Connect to the login node where your VNC server runs with SSH to tunnel your VNC session. {#6-connect-to-the-login-node-where-your-vnc-server-runs-with-ssh-to-tunnel-your-vnc-session}
-
-Tunnel the TCP port on which your VNC server is listenning.
-
-`
-$ ssh login2.anselm.it4i.cz -L 5901:localhost:5901
-`
-
-*If you use Windows and Putty, please refer to port forwarding setup
- in the documentation:*
-[https://docs.it4i.cz/anselm-cluster-documentation/accessing-the-cluster/x-window-and-vnc#section-12](accessing-the-cluster/x-window-and-vnc.html#section-12)
-
-#### 7. If you don't have Turbo VNC installed on your workstation. {#7-if-you-don-t-have-turbo-vnc-installed-on-your-workstation}
-
-Get it from: <http://sourceforge.net/projects/turbovnc/>
-
-#### 8. Run TurboVNC Viewer from your workstation. {#8-run-turbovnc-viewer-from-your-workstation}
-
-Mind that you should connect through the SSH tunneled port. In this
-example it is 5901 on your workstation (localhost).
-
-`
-$ vncviewer localhost:5901
-`
-
-*If you use Windows version of TurboVNC Viewer, just run the Viewer and
-use address **localhost:5901**.*
-
-#### 9. Proceed to the chapter "Access the visualization node." {#9-proceed-to-the-chapter-access-the-visualization-node}
-
-*Now you should have working TurboVNC session connected to your
-workstation.*
-
-#### 10. After you end your visualization session. {#10-after-you-end-your-visualization-session}
-
-*Don't forget to correctly shutdown your own VNC server on the login
-node!*
-
-`
-$ vncserver -kill :1
-`
-
-Access the visualization node
------------------------------
-
-To access the node use a dedicated PBS Professional scheduler queue
-qviz**. The queue has following properties:
-
- |queue |active project |project resources |nodes<th align="left">min ncpus*<th align="left">priority<th align="left">authorization<th align="left">walltime |
- | --- | --- |
- |<strong>qviz              </strong> Visualization queue\ |yes |none required |2 |4 |><em>150</em> |no |1 hour / 2 hours |
-
-Currently when accessing the node, each user gets 4 cores of a CPU
-allocated, thus approximately 16 GB of RAM and 1/4 of the GPU capacity.
-*If more GPU power or RAM is required, it is recommended to allocate one
-whole node per user, so that all 16 cores, whole RAM and whole GPU is
-exclusive. This is currently also the maximum allowed allocation per one
-user. One hour of work is allocated by default, the user may ask for 2
-hours maximum.*
-
-To access the visualization node, follow these steps:
-
-#### 1. In your VNC session, open a terminal and allocate a node using PBSPro qsub command. {#1-in-your-vnc-session-open-a-terminal-and-allocate-a-node-using-pbspro-qsub-command}
-
-*This step is necessary to allow you to proceed with next steps.*
-
-`
-$ qsub -I -q qviz -A PROJECT_ID
-`
-
-In this example the default values for CPU cores and usage time are
-used.
-
-`
-$ qsub -I -q qviz -A PROJECT_ID -l select=1:ncpus=16 -l walltime=02:00:00
-`
-
-*Substitute **PROJECT_ID** with the assigned project identification
-string.*
-
-In this example a whole node for 2 hours is requested.
-
-If there are free resources for your request, you will have a shell
-running on an assigned node. Please remember the name of the node.
-
-`
-$ uname -n
-srv8
-`
-
-In this example the visualization session was assigned to node **srv8**.
-
-#### 2. In your VNC session open another terminal (keep the one with interactive PBSPro job open). {#2-in-your-vnc-session-open-another-terminal-keep-the-one-with-interactive-pbspro-job-open}
-
-Setup the VirtualGL connection to the node, which PBSPro allocated for
-your job.
-
-`
-$ vglconnect srv8
-`
-
-You will be connected with created VirtualGL tunnel to the visualization
-node, where you will have a shell.
-
-#### 3. Load the VirtualGL module. {#3-load-the-virtualgl-module}
-
-`
-$ module load virtualgl/2.4
-`
-
-#### 4. Run your desired OpenGL accelerated application using VirtualGL script "vglrun". {#4-run-your-desired-opengl-accelerated-application-using-virtualgl-script-vglrun}
-
-`
-$ vglrun glxgears
-`
-
-Please note, that if you want to run an OpenGL application which is
-available through modules, you need at first load the respective module.
-E. g. to run the **Mentat** OpenGL application from **MARC** software
-package use:
-
-`
-$ module load marc/2013.1
-$ vglrun mentat
-`
-
-#### 5. After you end your work with the OpenGL application. {#5-after-you-end-your-work-with-the-opengl-application}
-
-Just logout from the visualization node and exit both opened terminals
-and end your VNC server session as described above.
-
-Tips and Tricks
----------------
-
-If you want to increase the responsibility of the visualization, please
-adjust your TurboVNC client settings in this way:
-
-
-
-To have an idea how the settings are affecting the resulting picture
-quality three levels of "JPEG image quality" are demonstrated:
-
-1. JPEG image quality = 30
-
-
-
-2. JPEG image quality = 15
-
-
-
-3. JPEG image quality = 10
-
-
-
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-Capacity computing
-==================
-
-
-
-Introduction
-------------
-
-In many cases, it is useful to submit huge (>100+) number of
-computational jobs into the PBS queue system. Huge number of (small)
-jobs is one of the most effective ways to execute embarrassingly
-parallel calculations, achieving best runtime, throughput and computer
-utilization.
-
-However, executing huge number of jobs via the PBS queue may strain the
-system. This strain may result in slow response to commands, inefficient
-scheduling and overall degradation of performance and user experience,
-for all users. For this reason, the number of jobs is **limited to 100
-per user, 1000 per job array**
-
-Please follow one of the procedures below, in case you wish to schedule
-more than >100 jobs at a time.
-
-- Use [Job arrays](capacity-computing.html#job-arrays)
- when running huge number of
- [multithread](capacity-computing.html#shared-jobscript-on-one-node)
- (bound to one node only) or multinode (multithread across
- several nodes) jobs
-- Use [GNU
- parallel](capacity-computing.html#gnu-parallel) when
- running single core jobs
-- Combine[GNU parallel with Job
- arrays](capacity-computing.html#combining-job-arrays-and-gnu-parallel)Â
- when running huge number of single core jobs
-
-Policy
-------
-
-1. A user is allowed to submit at most 100 jobs. Each job may be [a job
- array](capacity-computing.html#job-arrays).
-2. The array size is at most 1000 subjobs.
-
-Job arrays
---------------
-
-Huge number of jobs may be easily submitted and managed as a job array.
-
-A job array is a compact representation of many jobs, called subjobs.
-The subjobs share the same job script, and have the same values for all
-attributes and resources, with the following exceptions:
-
-- each subjob has a unique index, $PBS_ARRAY_INDEX
-- job Identifiers of subjobs only differ by their indices
-- the state of subjobs can differ (R,Q,...etc.)
-
-All subjobs within a job array have the same scheduling priority and
-schedule as independent jobs.
-Entire job array is submitted through a single qsub command and may be
-managed by qdel, qalter, qhold, qrls and qsig commands as a single job.
-
-### Shared jobscript
-
-All subjobs in job array use the very same, single jobscript. Each
-subjob runs its own instance of the jobscript. The instances execute
-different work controlled by $PBS_ARRAY_INDEX variable.
-
-Example:
-
-Assume we have 900 input files with name beginning with "file" (e. g.
-file001, ..., file900). Assume we would like to use each of these input
-files with program executable myprog.x, each as a separate job.
-
-First, we create a tasklist file (or subjobs list), listing all tasks
-(subjobs) - all input files in our example:
-
-`
-$ find . -name 'file*' > tasklist
-`
-
-Then we create jobscript:
-
-`
-#!/bin/bash
-#PBS -A PROJECT_ID
-#PBS -q qprod
-#PBS -l select=1:ncpus=16,walltime=02:00:00
-
-# change to local scratch directory
-SCR=/lscratch/$PBS_JOBID
-mkdir -p $SCR ; cd $SCR || exit
-
-# get individual tasks from tasklist with index from PBS JOB ARRAY
-TASK=$(sed -n "${PBS_ARRAY_INDEX}p" $PBS_O_WORKDIR/tasklist)
-
-# copy input file and executable to scratch
-cp $PBS_O_WORKDIR/$TASK input ; cp $PBS_O_WORKDIR/myprog.x .
-
-# execute the calculation
-./myprog.x < input > output
-
-# copy output file to submit directory
-cp output $PBS_O_WORKDIR/$TASK.out
-`
-
-In this example, the submit directory holds the 900 input files,
-executable myprog.x and the jobscript file. As input for each run, we
-take the filename of input file from created tasklist file. We copy the
-input file to local scratch /lscratch/$PBS_JOBID, execute the myprog.x
-and copy the output file back to >the submit directory,
-under the $TASK.out name. The myprog.x runs on one node only and must
-use threads to run in parallel. Be aware, that if the myprog.x **is not
-multithreaded**, then all the **jobs are run as single thread programs
-in sequential** manner. Due to allocation of the whole node, the
-accounted time is equal to the usage of whole node**, while using only
-1/16 of the node!
-
-If huge number of parallel multicore (in means of multinode multithread,
-e. g. MPI enabled) jobs is needed to run, then a job array approach
-should also be used. The main difference compared to previous example
-using one node is that the local scratch should not be used (as it's not
-shared between nodes) and MPI or other technique for parallel multinode
-run has to be used properly.
-
-### Submit the job array
-
-To submit the job array, use the qsub -J command. The 900 jobs of the
-[example above](capacity-computing.html#array_example) may
-be submitted like this:
-
-`
-$ qsub -N JOBNAME -J 1-900 jobscript
-12345[].dm2
-`
-
-In this example, we submit a job array of 900 subjobs. Each subjob will
-run on full node and is assumed to take less than 2 hours (please note
-the #PBS directives in the beginning of the jobscript file, dont'
-forget to set your valid PROJECT_ID and desired queue).
-
-Sometimes for testing purposes, you may need to submit only one-element
-array. This is not allowed by PBSPro, but there's a workaround:
-
-`
-$ qsub -N JOBNAME -J 9-10:2 jobscript
-`
-
-This will only choose the lower index (9 in this example) for
-submitting/running your job.
-
-### Manage the job array
-
-Check status of the job array by the qstat command.
-
-`
-$ qstat -a 12345[].dm2
-
-dm2:
- Req'd Req'd Elap
-Job ID Username Queue Jobname SessID NDS TSK Memory Time S Time
---------------- -------- -- |---|---| ------ --- --- ------ ----- - -----
-12345[].dm2 user2 qprod xx 13516 1 16 -- 00:50 B 00:02
-`
-
-The status B means that some subjobs are already running.
-
-Check status of the first 100 subjobs by the qstat command.
-
-`
-$ qstat -a 12345[1-100].dm2
-
-dm2:
- Req'd Req'd Elap
-Job ID Username Queue Jobname SessID NDS TSK Memory Time S Time
---------------- -------- -- |---|---| ------ --- --- ------ ----- - -----
-12345[1].dm2 user2 qprod xx 13516 1 16 -- 00:50 R 00:02
-12345[2].dm2 user2 qprod xx 13516 1 16 -- 00:50 R 00:02
-12345[3].dm2 user2 qprod xx 13516 1 16 -- 00:50 R 00:01
-12345[4].dm2 user2 qprod xx 13516 1 16 -- 00:50 Q --
- . . . . . . . . . . .
- , . . . . . . . . . .
-12345[100].dm2 user2 qprod xx 13516 1 16 -- 00:50 Q --
-`
-
-Delete the entire job array. Running subjobs will be killed, queueing
-subjobs will be deleted.
-
-`
-$ qdel 12345[].dm2
-`
-
-Deleting large job arrays may take a while.
-
-Display status information for all user's jobs, job arrays, and subjobs.
-
-`
-$ qstat -u $USER -t
-`
-
-Display status information for all user's subjobs.
-
-`
-$ qstat -u $USER -tJ
-`
-
-Read more on job arrays in the [PBSPro Users
-guide](../../pbspro-documentation.html).
-
-GNU parallel
-----------------
-
-Use GNU parallel to run many single core tasks on one node.
-
-GNU parallel is a shell tool for executing jobs in parallel using one or
-more computers. A job can be a single command or a small script that has
-to be run for each of the lines in the input. GNU parallel is most
-useful in running single core jobs via the queue system on Anselm.
-
-For more information and examples see the parallel man page:
-
-`
-$ module add parallel
-$ man parallel
-`
-
-### GNU parallel jobscript
-
-The GNU parallel shell executes multiple instances of the jobscript
-using all cores on the node. The instances execute different work,
-controlled by the $PARALLEL_SEQ variable.
-
-Example:
-
-Assume we have 101 input files with name beginning with "file" (e. g.
-file001, ..., file101). Assume we would like to use each of these input
-files with program executable myprog.x, each as a separate single core
-job. We call these single core jobs tasks.
-
-First, we create a tasklist file, listing all tasks - all input files in
-our example:
-
-`
-$ find . -name 'file*' > tasklist
-`
-
-Then we create jobscript:
-
-`
-#!/bin/bash
-#PBS -A PROJECT_ID
-#PBS -q qprod
-#PBS -l select=1:ncpus=16,walltime=02:00:00
-
-[ -z "$PARALLEL_SEQ" ] &&
-{ module add parallel ; exec parallel -a $PBS_O_WORKDIR/tasklist $0 ; }
-
-# change to local scratch directory
-SCR=/lscratch/$PBS_JOBID/$PARALLEL_SEQ
-mkdir -p $SCR ; cd $SCR || exit
-
-# get individual task from tasklist
-TASK=$1 Â
-
-# copy input file and executable to scratch
-cp $PBS_O_WORKDIR/$TASK input
-
-# execute the calculation
-cat input > output
-
-# copy output file to submit directory
-cp output $PBS_O_WORKDIR/$TASK.out
-`
-
-In this example, tasks from tasklist are executed via the GNU
-parallel. The jobscript executes multiple instances of itself in
-parallel, on all cores of the node. Once an instace of jobscript is
-finished, new instance starts until all entries in tasklist are
-processed. Currently processed entry of the joblist may be retrieved via
-$1 variable. Variable $TASK expands to one of the input filenames from
-tasklist. We copy the input file to local scratch, execute the myprog.x
-and copy the output file back to the submit directory, under the
-$TASK.out name.Â
-
-### Submit the job
-
-To submit the job, use the qsub command. The 101 tasks' job of the
-[example above](capacity-computing.html#gp_example) may be
-submitted like this:
-
-`
-$ qsub -N JOBNAME jobscript
-12345.dm2
-`
-
-In this example, we submit a job of 101 tasks. 16 input files will be
-processed in parallel. The 101 tasks on 16 cores are assumed to
-complete in less than 2 hours.
-
-Please note the #PBS directives in the beginning of the jobscript file,
-dont' forget to set your valid PROJECT_ID and desired queue.
-
-Job arrays and GNU parallel
--------------------------------
-
-Combine the Job arrays and GNU parallel for best throughput of single
-core jobs
-
-While job arrays are able to utilize all available computational nodes,
-the GNU parallel can be used to efficiently run multiple single-core
-jobs on single node. The two approaches may be combined to utilize all
-available (current and future) resources to execute single core jobs.
-
-Every subjob in an array runs GNU parallel to utilize all cores on the
-node
-
-### GNU parallel, shared jobscript
-
-Combined approach, very similar to job arrays, can be taken. Job array
-is submitted to the queuing system. The subjobs run GNU parallel. The
-GNU parallel shell executes multiple instances of the jobscript using
-all cores on the node. The instances execute different work, controlled
-by the $PBS_JOB_ARRAY and $PARALLEL_SEQ variables.
-
-Example:
-
-Assume we have 992 input files with name beginning with "file" (e. g.
-file001, ..., file992). Assume we would like to use each of these input
-files with program executable myprog.x, each as a separate single core
-job. We call these single core jobs tasks.
-
-First, we create a tasklist file, listing all tasks - all input files in
-our example:
-
-`
-$ find . -name 'file*' > tasklist
-`
-
-Next we create a file, controlling how many tasks will be executed in
-one subjob
-
-`
-$ seq 32 > numtasks
-`
-
-Then we create jobscript:
-
-`
-#!/bin/bash
-#PBS -A PROJECT_ID
-#PBS -q qprod
-#PBS -l select=1:ncpus=16,walltime=02:00:00
-
-[ -z "$PARALLEL_SEQ" ] &&
-{ module add parallel ; exec parallel -a $PBS_O_WORKDIR/numtasks $0 ; }
-
-# change to local scratch directory
-SCR=/lscratch/$PBS_JOBID/$PARALLEL_SEQ
-mkdir -p $SCR ; cd $SCR || exit
-
-# get individual task from tasklist with index from PBS JOB ARRAY and index form Parallel
-IDX=$(($PBS_ARRAY_INDEX + $PARALLEL_SEQ - 1))
-TASK=$(sed -n "${IDX}p" $PBS_O_WORKDIR/tasklist)
-[ -z "$TASK" ] && exit
-
-# copy input file and executable to scratch
-cp $PBS_O_WORKDIR/$TASK input
-
-# execute the calculation
-cat input > output
-
-# copy output file to submit directory
-cp output $PBS_O_WORKDIR/$TASK.out
-`
-
-In this example, the jobscript executes in multiple instances in
-parallel, on all cores of a computing node. Variable $TASK expands to
-one of the input filenames from tasklist. We copy the input file to
-local scratch, execute the myprog.x and copy the output file back to the
-submit directory, under the $TASK.out name. The numtasks file controls
-how many tasks will be run per subjob. Once an task is finished, new
-task starts, until the number of tasks in numtasks file is reached.
-
-Select subjob walltime and number of tasks per subjob carefully
-
-Â When deciding this values, think about following guiding rules :
-
-1. Let n=N/16. Inequality (n+1) * T < W should hold. The N is
- number of tasks per subjob, T is expected single task walltime and W
- is subjob walltime. Short subjob walltime improves scheduling and
- job throughput.
-2. Number of tasks should be modulo 16.
-3. These rules are valid only when all tasks have similar task
- walltimes T.
-
-### Submit the job array
-
-To submit the job array, use the qsub -J command. The 992 tasks' job of
-the [example
-above](capacity-computing.html#combined_example) may be
-submitted like this:
-
-`
-$ qsub -N JOBNAME -J 1-992:32 jobscript
-12345[].dm2
-`
-
-In this example, we submit a job array of 31 subjobs. Note the -J
-1-992:**32**, this must be the same as the number sent to numtasks file.
-Each subjob will run on full node and process 16 input files in
-parallel, 32 in total per subjob. Every subjob is assumed to complete
-in less than 2 hours.
-
-Please note the #PBS directives in the beginning of the jobscript file,
-dont' forget to set your valid PROJECT_ID and desired queue.
-
-Examples
---------
-
-Download the examples in
-[capacity.zip](capacity-computing-examples),Â
-illustrating the above listed ways to run huge number of jobs. We
-recommend to try out the examples, before using this for running
-production jobs.
-
-Unzip the archive in an empty directory on Anselm and follow the
-instructions in the README file
-
-`
-$ unzip capacity.zip
-$ cat README
-`
-
-Â
-
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-Resource Allocation and Job Execution
-=====================================
-
-
-
-To run a [job](../introduction.html), [computational
-resources](../introduction.html) for this particular job
-must be allocated. This is done via the PBS Pro job workload manager
-software, which efficiently distributes workloads across the
-supercomputer. Extensive informations about PBS Pro can be found in the
-[official documentation
-here](../../pbspro-documentation.html), especially in
-the [PBS Pro User's
-Guide](https://docs.it4i.cz/pbspro-documentation/pbspro-users-guide).
-
-Resources Allocation Policy
----------------------------
-
-The resources are allocated to the job in a fairshare fashion, subject
-to constraints set by the queue and resources available to the Project.
-[The Fairshare](job-priority.html) at Anselm ensures
-that individual users may consume approximately equal amount of
-resources per week. The resources are accessible via several queues for
-queueing the jobs. The queues provide prioritized and exclusive access
-to the computational resources. Following queues are available to Anselm
-users:
-
-- **qexp**, the \
-- **qprod**, the \***
-- **qlong**, the Long queue, regula
-- **qnvidia, qmic, qfat**, the Dedicated queues
-- **qfree,** the Free resource utilization queue
-
-Check the queue status at <https://extranet.it4i.cz/anselm/>
-
-Read more on the [Resource Allocation
-Policy](resources-allocation-policy.html) page.
-
-Job submission and execution
-----------------------------
-
-Use the **qsub** command to submit your jobs.
-
-The qsub submits the job into the queue. The qsub command creates a
-request to the PBS Job manager for allocation of specified resources.Â
-The **smallest allocation unit is entire node, 16 cores**, with
-exception of the qexp queue. The resources will be allocated when
-available, subject to allocation policies and constraints. **After the
-resources are allocated the jobscript or interactive shell is executed
-on first of the allocated nodes.**
-
-Read more on the [Job submission and
-execution](job-submission-and-execution.html) page.
-
-Capacity computing
-------------------
-
-Use Job arrays when running huge number of jobs.
-Use GNU Parallel and/or Job arrays when running (many) single core jobs.
-
-In many cases, it is useful to submit huge (>100+) number of
-computational jobs into the PBS queue system. Huge number of (small)
-jobs is one of the most effective ways to execute embarrassingly
-parallel calculations, achieving best runtime, throughput and computer
-utilization. In this chapter, we discuss the the recommended way to run
-huge number of jobs, including **ways to run huge number of single core
-jobs**.
-
-Read more on [Capacity
-computing](capacity-computing.html) page.
-
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-Job scheduling
-==============
-
-Job execution priority
-----------------------
-
-Scheduler gives each job an execution priority and then uses this job
-execution priority to select which job(s) to run.
-
-Job execution priority on Anselm is determined by these job properties
-(in order of importance):
-
-1. queue priority
-2. fairshare priority
-3. eligible time
-
-### Queue priority
-
-Queue priority is priority of queue where job is queued before
-execution.
-
-Queue priority has the biggest impact on job execution priority.
-Execution priority of jobs in higher priority queues is always greater
-than execution priority of jobs in lower priority queues. Other
-properties of job used for determining job execution priority (fairshare
-priority, eligible time) cannot compete with queue priority.
-
-Queue priorities can be seen at <https://extranet.it4i.cz/anselm/queues>
-
-### Fairshare priority
-
-Fairshare priority is priority calculated on recent usage of resources.
-Fairshare priority is calculated per project, all members of project
-share same fairshare priority. Projects with higher recent usage have
-lower fairshare priority than projects with lower or none recent usage.
-
-Fairshare priority is used for ranking jobs with equal queue priority.
-
-Fairshare priority is calculated as
-
-
-
-where MAX_FAIRSHARE has value 1E6,
-usage~Project~ is cumulated usage by all members of selected project,
-usage~Total~ is total usage by all users, by all projects.
-
-Usage counts allocated corehours (ncpus*walltime). Usage is decayed, or
-cut in half periodically, at the interval 168 hours (one week).
-Jobs queued in queue qexp are not calculated to project's usage.
-
-Calculated usage and fairshare priority can be seen at
-<https://extranet.it4i.cz/anselm/projects>.
-
-Calculated fairshare priority can be also seen as
-Resource_List.fairshare attribute of a job.
-
-###Eligible time
-
-Eligible time is amount (in seconds) of eligible time job accrued while
-waiting to run. Jobs with higher eligible time gains higher
-priority.
-
-Eligible time has the least impact on execution priority. Eligible time
-is used for sorting jobs with equal queue priority and fairshare
-priority. It is very, very difficult for >eligible time to
-compete with fairshare priority.
-
-Eligible time can be seen as eligible_time attribute of
-job.
-
-### Formula
-
-Job execution priority (job sort formula) is calculated as:
-
-
-
-### Job backfilling
-
-Anselm cluster uses job backfilling.
-
-Backfilling means fitting smaller jobs around the higher-priority jobs
-that the scheduler is going to run next, in such a way that the
-higher-priority jobs are not delayed. Backfilling allows us to keep
-resources from becoming idle when the top job (job with the highest
-execution priority) cannot run.
-
-The scheduler makes a list of jobs to run in order of execution
-priority. Scheduler looks for smaller jobs that can fit into the usage
-gaps
-around the highest-priority jobs in the list. The scheduler looks in the
-prioritized list of jobs and chooses the highest-priority smaller jobs
-that fit. Filler jobs are run only if they will not delay the start time
-of top jobs.
-
-It means, that jobs with lower execution priority can be run before jobs
-with higher execution priority.
-
-It is **very beneficial to specify the walltime** when submitting jobs.
-
-Specifying more accurate walltime enables better schedulling, better
-execution times and better resource usage. Jobs with suitable (small)
-walltime could be backfilled - and overtake job(s) with higher priority.
-
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-Job submission and execution
-============================
-
-
-
-Job Submission
---------------
-
-When allocating computational resources for the job, please specify
-
-1. suitable queue for your job (default is qprod)
-2. number of computational nodes required
-3. number of cores per node required
-4. maximum wall time allocated to your calculation, note that jobs
- exceeding maximum wall time will be killed
-5. Project ID
-6. Jobscript or interactive switch
-
-Use the **qsub** command to submit your job to a queue for allocation of
-the computational resources.
-
-Submit the job using the qsub command:
-
-`
-$ qsub -A Project_ID -q queue -l select=x:ncpus=y,walltime=[[hh:]mm:]ss[.ms] jobscript
-`
-
-The qsub submits the job into the queue, in another words the qsub
-command creates a request to the PBS Job manager for allocation of
-specified resources. The resources will be allocated when available,
-subject to above described policies and constraints. **After the
-resources are allocated the jobscript or interactive shell is executed
-on first of the allocated nodes.**
-
-### Job Submission Examples
-
-`
-$ qsub -A OPEN-0-0 -q qprod -l select=64:ncpus=16,walltime=03:00:00 ./myjob
-`
-
-In this example, we allocate 64 nodes, 16 cores per node, for 3 hours.
-We allocate these resources via the qprod queue, consumed resources will
-be accounted to the Project identified by Project ID OPEN-0-0. Jobscript
-myjob will be executed on the first node in the allocation.
-
-Â
-
-`
-$ qsub -q qexp -l select=4:ncpus=16 -I
-`
-
-In this example, we allocate 4 nodes, 16 cores per node, for 1 hour. We
-allocate these resources via the qexp queue. The resources will be
-available interactively
-
-Â
-
-`
-$ qsub -A OPEN-0-0 -q qnvidia -l select=10:ncpus=16 ./myjob
-`
-
-In this example, we allocate 10 nvidia accelerated nodes, 16 cores per
-node, for 24 hours. We allocate these resources via the qnvidia queue.
-Jobscript myjob will be executed on the first node in the allocation.
-
-Â
-
-`
-$ qsub -A OPEN-0-0 -q qfree -l select=10:ncpus=16 ./myjob
-`
-
-In this example, we allocate 10Â nodes, 16 cores per node, for 12 hours.
-We allocate these resources via the qfree queue. It is not required that
-the project OPEN-0-0 has any available resources left. Consumed
-resources are still accounted for. Jobscript myjob will be executed on
-the first node in the allocation.
-
-Â
-
-All qsub options may be [saved directly into the
-jobscript](job-submission-and-execution.html#PBSsaved). In
-such a case, no options to qsub are needed.
-
-`
-$ qsub ./myjob
-`
-
-Â
-
-By default, the PBS batch system sends an e-mail only when the job is
-aborted. Disabling mail events completely can be done like this:
-
-`
-$ qsub -m n
-`
-
-Advanced job placement
-----------------------
-
-### Placement by name
-
-Specific nodes may be allocated via the PBS
-
-`
-qsub -A OPEN-0-0 -q qprod -l select=1:ncpus=16:host=cn171+1:ncpus=16:host=cn172 -I
-`
-
-In this example, we allocate nodes cn171 and cn172, all 16 cores per
-node, for 24 hours. Consumed resources will be accounted to the Project
-identified by Project ID OPEN-0-0. The resources will be available
-interactively.
-
-### Placement by CPU type
-
-Nodes equipped with Intel Xeon E5-2665 CPU have base clock frequency
-2.4GHz, nodes equipped with Intel Xeon E5-2470 CPU have base frequency
-2.3 GHz (see section Compute Nodes for details). Nodes may be selected
-via the PBS resource attribute
-cpu_freq .
-
- CPU Type base freq. Nodes cpu_freq attribute
- -------------- |---|---|-- ---------------------------- ---------------------
- Intel Xeon E5-2665 2.4GHz cn[1-180], cn[208-209] 24
- Intel Xeon E5-2470 2.3GHz cn[181-207] 23
-
-Â
-
-`
-$ qsub -A OPEN-0-0 -q qprod -l select=4:ncpus=16:cpu_freq=24 -I
-`
-
-In this example, we allocate 4 nodes, 16 cores, selecting only the nodes
-with Intel Xeon E5-2665 CPU.
-
-### Placement by IB switch
-
-Groups of computational nodes are connected to chassis integrated
-Infiniband switches. These switches form the leaf switch layer of the
-[Infiniband network](../network.html)
-fat tree topology. Nodes sharing the leaf
-switch can communicate most efficiently. Sharing the same switch
-prevents hops in the network and provides for unbiased, most efficient
-network communication.
-
-Nodes sharing the same switch may be selected via the PBS resource
-attribute ibswitch. Values of this attribute are iswXX, where XX is the
-switch number. The node-switch mapping can be seen at [Hardware
-Overview](../hardware-overview.html) section.
-
-We recommend allocating compute nodes of a single switch when best
-possible computational network performance is required to run the job
-efficiently:
-
- qsub -A OPEN-0-0 -q qprod -l select=18:ncpus=16:ibswitch=isw11 ./myjob
-
-In this example, we request all the 18 nodes sharing the isw11 switch
-for 24 hours. Full chassis will be allocated.
-
-Advanced job handling
----------------------
-
-### Selecting Turbo Boost off
-
-Intel Turbo Boost Technology is on by default. We strongly recommend
-keeping the default.Â
-
-If necessary (such as in case of benchmarking) you can disable the Turbo
-for all nodes of the job by using the PBS resource attribute
-cpu_turbo_boost
-
- $ qsub -A OPEN-0-0 -q qprod -l select=4:ncpus=16 -l cpu_turbo_boost=0 -I
-
-More about the Intel Turbo Boost in the TurboBoost section
-
-### Advanced examples
-
-In the following example, we select an allocation for benchmarking a
-very special and demanding MPI program. We request Turbo off, 2 full
-chassis of compute nodes (nodes sharing the same IB switches) for 30
-minutes:
-
- $ qsub -A OPEN-0-0 -q qprod
- -l select=18:ncpus=16:ibswitch=isw10:mpiprocs=1:ompthreads=16+18:ncpus=16:ibswitch=isw20:mpiprocs=16:ompthreads=1
- -l cpu_turbo_boost=0,walltime=00:30:00
- -N Benchmark ./mybenchmark
-
-The MPI processes will be distributed differently on the nodes connected
-to the two switches. On the isw10 nodes, we will run 1 MPI process per
-node 16 threads per process, on isw20Â nodes we will run 16 plain MPI
-processes.
-
-Although this example is somewhat artificial, it demonstrates the
-flexibility of the qsub command options.
-
-Job Management
---------------
-
-Check status of your jobs using the **qstat** and **check-pbs-jobs**
-commands
-
-`
-$ qstat -a
-$ qstat -a -u username
-$ qstat -an -u username
-$ qstat -f 12345.srv11
-`
-
-Example:
-
-`
-$ qstat -a
-
-srv11:
- Req'd Req'd Elap
-Job ID Username Queue Jobname SessID NDS TSK Memory Time S Time
---------------- -------- -- |---|---| ------ --- --- ------ ----- - -----
-16287.srv11 user1 qlong job1 6183 4 64 -- 144:0 R 38:25
-16468.srv11 user1 qlong job2 8060 4 64 -- 144:0 R 17:44
-16547.srv11 user2 qprod job3x 13516 2 32 -- 48:00 R 00:58
-`
-
-In this example user1 and user2 are running jobs named job1, job2 and
-job3x. The jobs job1 and job2 are using 4 nodes, 16 cores per node each.
-The job1 already runs for 38 hours and 25 minutes, job2 for 17 hours 44
-minutes. The job1 already consumed 64*38.41 = 2458.6 core hours. The
-job3x already consumed 0.96*32 = 30.93 core hours. These consumed core
-hours will be accounted on the respective project accounts, regardless
-of whether the allocated cores were actually used for computations.
-
-Check status of your jobs using check-pbs-jobs command. Check presence
-of user's PBS jobs' processes on execution hosts. Display load,
-processes. Display job standard and error output. Continuously display
-(tail -f) job standard or error output.
-
-`
-$ check-pbs-jobs --check-all
-$ check-pbs-jobs --print-load --print-processes
-$ check-pbs-jobs --print-job-out --print-job-err
-
-$ check-pbs-jobs --jobid JOBID --check-all --print-all
-
-$ check-pbs-jobs --jobid JOBID --tailf-job-out
-`
-
-Examples:
-
-`
-$ check-pbs-jobs --check-all
-JOB 35141.dm2, session_id 71995, user user2, nodes cn164,cn165
-Check session id: OK
-Check processes
-cn164: OK
-cn165: No process
-`
-
-In this example we see that job 35141.dm2 currently runs no process on
-allocated node cn165, which may indicate an execution error.
-
-`
-$ check-pbs-jobs --print-load --print-processes
-JOB 35141.dm2, session_id 71995, user user2, nodes cn164,cn165
-Print load
-cn164: LOAD: 16.01, 16.01, 16.00
-cn165: LOAD: 0.01, 0.00, 0.01
-Print processes
- %CPU CMD
-cn164: 0.0 -bash
-cn164: 0.0 /bin/bash /var/spool/PBS/mom_priv/jobs/35141.dm2.SC
-cn164: 99.7 run-task
-...
-`
-
-In this example we see that job 35141.dm2 currently runs process
-run-task on node cn164, using one thread only, while node cn165 is
-empty, which may indicate an execution error.
-
-`
-$ check-pbs-jobs --jobid 35141.dm2 --print-job-out
-JOB 35141.dm2, session_id 71995, user user2, nodes cn164,cn165
-Print job standard output:
-======================== Job start ==========================
-Started at   : Fri Aug 30 02:47:53 CEST 2013
-Script name  : script
-Run loop 1
-Run loop 2
-Run loop 3
-`
-
-In this example, we see actual output (some iteration loops) of the job
-35141.dm2
-
-Manage your queued or running jobs, using the **qhold**, **qrls**,
-qdel,** **qsig** or **qalter** commands
-
-You may release your allocation at any time, using qdel command
-
-`
-$ qdel 12345.srv11
-`
-
-You may kill a running job by force, using qsig command
-
-`
-$ qsig -s 9 12345.srv11
-`
-
-Learn more by reading the pbs man page
-
-`
-$ man pbs_professional
-`
-
-Job Execution
--------------
-
-### Jobscript
-
-Prepare the jobscript to run batch jobs in the PBS queue system
-
-The Jobscript is a user made script, controlling sequence of commands
-for executing the calculation. It is often written in bash, other
-scripts may be used as well. The jobscript is supplied to PBS **qsub**
-command as an argument and executed by the PBS Professional workload
-manager.
-
-The jobscript or interactive shell is executed on first of the allocated
-nodes.
-
-`
-$ qsub -q qexp -l select=4:ncpus=16 -N Name0 ./myjob
-$ qstat -n -u username
-
-srv11:
- Req'd Req'd Elap
-Job ID Username Queue Jobname SessID NDS TSK Memory Time S Time
---------------- -------- -- |---|---| ------ --- --- ------ ----- - -----
-15209.srv11 username qexp Name0 5530 4 64 -- 01:00 R 00:00
- cn17/0*16+cn108/0*16+cn109/0*16+cn110/0*16
-`
-
-Â In this example, the nodes cn17, cn108, cn109 and cn110 were allocated
-for 1 hour via the qexp queue. The jobscript myjob will be executed on
-the node cn17, while the nodes cn108, cn109 and cn110 are available for
-use as well.
-
-The jobscript or interactive shell is by default executed in home
-directory
-
-`
-$ qsub -q qexp -l select=4:ncpus=16 -I
-qsub: waiting for job 15210.srv11 to start
-qsub: job 15210.srv11 ready
-
-$ pwd
-/home/username
-`
-
-In this example, 4 nodes were allocated interactively for 1 hour via the
-qexp queue. The interactive shell is executed in the home directory.
-
-All nodes within the allocation may be accessed via ssh. Unallocated
-nodes are not accessible to user.
-
-The allocated nodes are accessible via ssh from login nodes. The nodes
-may access each other via ssh as well.
-
-Calculations on allocated nodes may be executed remotely via the MPI,
-ssh, pdsh or clush. You may find out which nodes belong to the
-allocation by reading the $PBS_NODEFILE file
-
-`
-qsub -q qexp -l select=4:ncpus=16 -I
-qsub: waiting for job 15210.srv11 to start
-qsub: job 15210.srv11 ready
-
-$ pwd
-/home/username
-
-$ sort -u $PBS_NODEFILE
-cn17.bullx
-cn108.bullx
-cn109.bullx
-cn110.bullx
-
-$ pdsh -w cn17,cn[108-110] hostname
-cn17: cn17
-cn108: cn108
-cn109: cn109
-cn110: cn110
-`
-
-In this example, the hostname program is executed via pdsh from the
-interactive shell. The execution runs on all four allocated nodes. The
-same result would be achieved if the pdsh is called from any of the
-allocated nodes or from the login nodes.
-
-### Example Jobscript for MPI Calculation
-
-Production jobs must use the /scratch directory for I/O
-
-The recommended way to run production jobs is to change to /scratch
-directory early in the jobscript, copy all inputs to /scratch, execute
-the calculations and copy outputs to home directory.
-
-`
-#!/bin/bash
-
-# change to scratch directory, exit on failure
-SCRDIR=/scratch/$USER/myjob
-mkdir -p $SCRDIR
-cd $SCRDIR || exit
-
-# copy input file to scratch
-cp $PBS_O_WORKDIR/input .
-cp $PBS_O_WORKDIR/mympiprog.x .
-
-# load the mpi module
-module load openmpi
-
-# execute the calculation
-mpiexec -pernode ./mympiprog.x
-
-# copy output file to home
-cp output $PBS_O_WORKDIR/.
-
-#exit
-exit
-`
-
-In this example, some directory on the /home holds the input file input
-and executable mympiprog.x . We create a directory myjob on the /scratch
-filesystem, copy input and executable files from the /home directory
-where the qsub was invoked ($PBS_O_WORKDIR) to /scratch, execute the
-MPI programm mympiprog.x and copy the output file back to the /home
-directory. The mympiprog.x is executed as one process per node, on all
-allocated nodes.
-
-Consider preloading inputs and executables onto [shared
-scratch](../storage.html) before the calculation starts.
-
-In some cases, it may be impractical to copy the inputs to scratch and
-outputs to home. This is especially true when very large input and
-output files are expected, or when the files should be reused by a
-subsequent calculation. In such a case, it is users responsibility to
-preload the input files on shared /scratch before the job submission and
-retrieve the outputs manually, after all calculations are finished.
-
-Store the qsub options within the jobscript.
-Use **mpiprocs** and **ompthreads** qsub options to control the MPI job
-execution.
-
-Example jobscript for an MPI job with preloaded inputs and executables,
-options for qsub are stored within the script :
-
-`
-#!/bin/bash
-#PBS -q qprod
-#PBS -N MYJOB
-#PBS -l select=100:ncpus=16:mpiprocs=1:ompthreads=16
-#PBS -A OPEN-0-0
-
-# change to scratch directory, exit on failure
-SCRDIR=/scratch/$USER/myjob
-cd $SCRDIR || exit
-
-# load the mpi module
-module load openmpi
-
-# execute the calculation
-mpiexec ./mympiprog.x
-
-#exit
-exit
-`
-
-In this example, input and executable files are assumed preloaded
-manually in /scratch/$USER/myjob directory. Note the **mpiprocs** and
-ompthreads** qsub options, controlling behavior of the MPI execution.
-The mympiprog.x is executed as one process per node, on all 100
-allocated nodes. If mympiprog.x implements OpenMP threads, it will run
-16 threads per node.
-
-More information is found in the [Running
-OpenMPI](../software/mpi-1/Running_OpenMPI.html) and
-[Running MPICH2](../software/mpi-1/running-mpich2.html)
-sections.
-
-### Example Jobscript for Single Node Calculation
-
-Local scratch directory is often useful for single node jobs. Local
-scratch will be deleted immediately after the job ends.
-
-Example jobscript for single node calculation, using [local
-scratch](../storage.html) on the node:
-
-`
-#!/bin/bash
-
-# change to local scratch directory
-cd /lscratch/$PBS_JOBID || exit
-
-# copy input file to scratch
-cp $PBS_O_WORKDIR/input .
-cp $PBS_O_WORKDIR/myprog.x .
-
-# execute the calculation
-./myprog.x
-
-# copy output file to home
-cp output $PBS_O_WORKDIR/.
-
-#exit
-exit
-`
-
-In this example, some directory on the home holds the input file input
-and executable myprog.x . We copy input and executable files from the
-home directory where the qsub was invoked ($PBS_O_WORKDIR) to local
-scratch /lscratch/$PBS_JOBID, execute the myprog.x and copy the output
-file back to the /home directory. The myprog.x runs on one node only and
-may use threads.
-
-### Other Jobscript Examples
-
-Further jobscript examples may be found in the
-[Software](../software.1.html) section and the [Capacity
-computing](capacity-computing.html) section.
-
-Â
-
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-Resources Allocation Policy
-===========================
-
-
-
-Resources Allocation Policy
----------------------------
-
-The resources are allocated to the job in a fairshare fashion, subject
-to constraints set by the queue and resources available to the Project.
-The Fairshare at Anselm ensures that individual users may consume
-approximately equal amount of resources per week. Detailed information
-in the [Job scheduling](job-priority.html) section. The
-resources are accessible via several queues for queueing the jobs. The
-queues provide prioritized and exclusive access to the computational
-resources. Following table provides the queue partitioning overview:Â Â
-Â
-
- |queue |active project |project resources |nodes<th align="left">min ncpus*<th align="left">priority<th align="left">authorization<th align="left">walltime |
- | --- | --- |
- |<strong>qexp</strong>\ |no |none required |2 reserved, 31 totalincluding MIC, GPU and FAT nodes |1 |><em>150</em> |no |1h |
- |<strong>qprod</strong>\ |yes |> 0 |><em>178 nodes w/o accelerator</em>\ |16 |0 |no |24/48h |
- |<strong>qlong</strong>Long queue\ |yes |> 0 |60 nodes w/o accelerator |16 |0 |no |72/144h |
- |<strong>qnvidia, qmic, qfat</strong>Dedicated queues\ |yes |<p>> 0\ |23 total qnvidia4 total qmic2 total qfat |16 |><em>200</em> |yes |24/48h |
- |<strong>qfree</strong>\ |yes |none required |178 w/o accelerator |16 |-1024 |no |12h |
-
-The qfree queue is not free of charge**. [Normal
-accounting](resources-allocation-policy.html#resources-accounting-policy)
-applies. However, it allows for utilization of free resources, once a
-Project exhausted all its allocated computational resources. This does
-not apply for Directors Discreation's projects (DD projects) by default.
-Usage of qfree after exhaustion of DD projects computational resources
-is allowed after request for this queue.
-
-The qexp queue is equipped with the nodes not having the very same CPU
-clock speed.** Should you need the very same CPU speed, you have to
-select the proper nodes during the PSB job submission.
-**
-
-- **qexp**, the \: This queue is dedicated for testing and
- running very small jobs. It is not required to specify a project to
- enter the qexp. >*>There are 2 nodes always reserved for
- this queue (w/o accelerator), maximum 8 nodes are available via the
- qexp for a particular user, from a pool of nodes containing
- **Nvidia** accelerated nodes (cn181-203), **MIC** accelerated
- nodes (cn204-207) and **Fat** nodes with 512GB RAM (cn208-209). This
- enables to test and tune also accelerated code or code with higher
- RAM requirements.* The nodes may be allocated on per
- core basis. No special authorization is required to use it. The
- maximum runtime in qexp is 1 hour.
-- **qprod**, the \***: This queue is intended for
- normal production runs. It is required that active project with
- nonzero remaining resources is specified to enter the qprod. All
- nodes may be accessed via the qprod queue, except the reserved ones.
- >*>178 nodes without accelerator are
- included.*Â Full nodes, 16 cores per node
- are allocated. The queue runs with medium priority and no special
- authorization is required to use it. The maximum runtime in qprod is
- 48 hours.
-- **qlong**, the Long queue***: This queue is intended for long
- production runs. It is required that active project with nonzero
- remaining resources is specified to enter the qlong. Only 60 nodes
- without acceleration may be accessed via the qlong queue. Full
- nodes, 16 cores per node are allocated. The queue runs with medium
- priority and no special authorization is required to use it.>
- *The maximum runtime in qlong is 144 hours (three times of the
- standard qprod time - 3 * 48 h).*
-- **qnvidia, qmic, qfat**, the Dedicated queues***: The queue qnvidia
- is dedicated to access the Nvidia accelerated nodes, the qmic to
- access MIC nodes and qfat the Fat nodes. It is required that active
- project with nonzero remaining resources is specified to enter
- these queues. 23 nvidia, 4 mic and 2 fat nodes are included. Full
- nodes, 16 cores per node are allocated. The queues run with>
- *very high priority*, the jobs will be scheduled before the
- jobs coming from the> *qexp* queue. An PI> *needs
- explicitly* ask
- [support](https://support.it4i.cz/rt/) for
- authorization to enter the dedicated queues for all users associated
- to her/his Project.
-- **qfree**, The \***: The queue qfree is intended
- for utilization of free resources, after a Project exhausted all its
- allocated computational resources (Does not apply to DD projects
- by default. DD projects have to request for persmission on qfree
- after exhaustion of computational resources.). It is required that
- active project is specified to enter the queue, however no remaining
- resources are required. Consumed resources will be accounted to
- the Project. Only 178 nodes without accelerator may be accessed from
- this queue. Full nodes, 16 cores per node are allocated. The queue
- runs with very low priority and no special authorization is required
- to use it. The maximum runtime in qfree is 12 hours.
-
-### Notes
-
-The job wall clock time defaults to **half the maximum time**, see table
-above. Longer wall time limits can be [set manually, see
-examples](job-submission-and-execution.html).
-
-Jobs that exceed the reserved wall clock time (Req'd Time) get killed
-automatically. Wall clock time limit can be changed for queuing jobs
-(state Q) using the qalter command, however can not be changed for a
-running job (state R).
-
-Anselm users may check current queue configuration at
-<https://extranet.it4i.cz/anselm/queues>.
-
-### Queue status
-
-Check the status of jobs, queues and compute nodes at
-<https://extranet.it4i.cz/anselm/>
-
-
-
-Display the queue status on Anselm:
-
-`
-$ qstat -q
-`
-
-The PBS allocation overview may be obtained also using the rspbs
-command.
-
-`
-$ rspbs
-Usage: rspbs [options]
-
-Options:
- --version            show program's version number and exit
- -h, --help           show this help message and exit
-Â --get-node-ncpu-chart
-Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Print chart of allocated ncpus per node
- --summary            Print summary
- --get-server-details Print server
- --get-queues         Print queues
- --get-queues-details Print queues details
- --get-reservations   Print reservations
-Â --get-reservations-details
-Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Print reservations details
- --get-nodes          Print nodes of PBS complex
- --get-nodeset        Print nodeset of PBS complex
- --get-nodes-details  Print nodes details
- --get-jobs           Print jobs
- --get-jobs-details   Print jobs details
-Â --get-jobs-check-params
-Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Print jobid, job state, session_id, user, nodes
- --get-users          Print users of jobs
-Â --get-allocated-nodes
-Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Print allocated nodes of jobs
-Â --get-allocated-nodeset
-Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Print allocated nodeset of jobs
- --get-node-users     Print node users
- --get-node-jobs      Print node jobs
- --get-node-ncpus     Print number of ncpus per node
-Â --get-node-allocated-ncpus
-Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Print number of allocated ncpus per node
- --get-node-qlist     Print node qlist
- --get-node-ibswitch  Print node ibswitch
- --get-user-nodes     Print user nodes
- --get-user-nodeset   Print user nodeset
- --get-user-jobs      Print user jobs
- --get-user-jobc      Print number of jobs per user
- --get-user-nodec     Print number of allocated nodes per user
- --get-user-ncpus     Print number of allocated ncpus per user
- --get-qlist-nodes    Print qlist nodes
- --get-qlist-nodeset  Print qlist nodeset
- --get-ibswitch-nodes Print ibswitch nodes
-Â --get-ibswitch-nodeset
-Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Print ibswitch nodeset
-Â --state=STATEÂ Â Â Â Â Â Â Â Only for given job state
-Â --jobid=JOBIDÂ Â Â Â Â Â Â Â Only for given job ID
-Â --user=USERÂ Â Â Â Â Â Â Â Â Â Only for given user
-Â --node=NODEÂ Â Â Â Â Â Â Â Â Â Only for given node
-Â --nodestate=NODESTATE
-Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Only for given node state (affects only --get-node*
-Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â --get-qlist-* --get-ibswitch-* actions)
- --incl-finished      Include finished jobs
-`
-
-Resources Accounting Policy
--------------------------------
-
-### The Core-Hour
-
-The resources that are currently subject to accounting are the
-core-hours. The core-hours are accounted on the wall clock basis. The
-accounting runs whenever the computational cores are allocated or
-blocked via the PBS Pro workload manager (the qsub command), regardless
-of whether the cores are actually used for any calculation. 1 core-hour
-is defined as 1 processor core allocated for 1 hour of wall clock time.
-Allocating a full node (16 cores) for 1 hour accounts to 16 core-hours.
-See example in the [Job submission and
-execution](job-submission-and-execution.html) section.
-
-### Check consumed resources
-
-The **it4ifree** command is a part of it4i.portal.clients package,
-located here:
-<https://pypi.python.org/pypi/it4i.portal.clients>
-
-User may check at any time, how many core-hours have been consumed by
-himself/herself and his/her projects. The command is available on
-clusters' login nodes.
-
-`
-$ it4ifree
-Password:
-    PID  Total Used ...by me Free
-Â Â -------- ------- ------ -------- -------
-Â Â OPEN-0-0 1500000 400644Â Â 225265 1099356
-Â Â DD-13-1 Â Â 10000 2606 2606 7394
-`
-
-Â
-
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-Overview of ANSYS Products
-==========================
-
-[SVS FEM](http://www.svsfem.cz/)** as **[ANSYS
-Channel partner](http://www.ansys.com/)**Â for Czech
-Republic provided all ANSYS licenses for ANSELM cluster and supports of
-all ANSYS Products (Multiphysics, Mechanical, MAPDL, CFX, Fluent,
-Maxwell, LS-DYNA...) to IT staff and ANSYS users. If you are challenging
-to problem of ANSYS functionality contact
-please [hotline@svsfem.cz](mailto:hotline@svsfem.cz?subject=Ostrava%20-%20ANSELM)
-
-Anselm provides as commercial as academic variants. Academic variants
-are distinguished by "**Academic...**" word in the name of  license or
-by two letter preposition "**aa_**" in the license feature name. Change
-of license is realized on command line respectively directly in user's
-pbs file (see individual products). [
- More about
-licensing
-here](ansys/licensing.html)
-
-To load the latest version of any ANSYS product (Mechanical, Fluent,
-CFX, MAPDL,...) load the module:
-
- $ module load ansys
-
-ANSYS supports interactive regime, but due to assumed solution of
-extremely difficult tasks it is not recommended.
-
-If user needs to work in interactive regime we recommend to configure
-the RSM service on the client machine which allows to forward the
-solution to the Anselm directly from the client's Workbench project
-(see ANSYS RSM service).
-
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-ANSYS CFX
-=========
-
-[ANSYS
-CFX](http://www.ansys.com/Products/Simulation+Technology/Fluid+Dynamics/Fluid+Dynamics+Products/ANSYS+CFX)
-software is a high-performance, general purpose fluid dynamics program
-that has been applied to solve wide-ranging fluid flow problems for over
-20 years. At the heart of ANSYS CFX is its advanced solver technology,
-the key to achieving reliable and accurate solutions quickly and
-robustly. The modern, highly parallelized solver is the foundation for
-an abundant choice of physical models to capture virtually any type of
-phenomena related to fluid flow. The solver and its many physical models
-are wrapped in a modern, intuitive, and flexible GUI and user
-environment, with extensive capabilities for customization and
-automation using session files, scripting and a powerful expression
-language.
-
-To run ANSYS CFX in batch mode you can utilize/modify the default
-cfx.pbs script and execute it via the qsub command.
-
-`
-#!/bin/bash
-#PBS -l nodes=2:ppn=16
-#PBS -q qprod
-#PBS -N $USER-CFX-Project
-#PBS -A XX-YY-ZZ
-
-#! Mail to user when job terminate or abort
-#PBS -m ae
-
-#!change the working directory (default is home directory)
-#cd <working directory> (working directory must exists)
-WORK_DIR="/scratch/$USER/work"
-cd $WORK_DIR
-
-echo Running on host `hostname`
-echo Time is `date`
-echo Directory is `pwd`
-echo This jobs runs on the following processors:
-echo `cat $PBS_NODEFILE`
-
-module load ansys
-
-#### Set number of processors per host listing
-#### (set to 1 as $PBS_NODEFILE lists each node twice if :ppn=2)
-procs_per_host=1
-#### Create host list
-hl=""
-for host in `cat $PBS_NODEFILE`
-do
- if [ "$hl" = "" ]
- then hl="$host:$procs_per_host"
- else hl="${hl}:$host:$procs_per_host"
- fi
-done
-
-echo Machines: $hl
-
-#-dev input.def includes the input of CFX analysis in DEF format
-#-P the name of prefered license feature (aa_r=ANSYS Academic Research, ane3fl=Multiphysics(commercial))
-/ansys_inc/v145/CFX/bin/cfx5solve -def input.def -size 4 -size-ni 4x -part-large -start-method "Platform MPI Distributed Parallel" -par-dist $hl -P aa_r
-`
-
-Header of the pbs file (above) is common and description can be find
-on [this
-site](../../resource-allocation-and-job-execution/job-submission-and-execution.html).
-SVS FEM recommends to utilize sources by keywords: nodes, ppn. These
-keywords allows to address directly the number of nodes (computers) and
-cores (ppn) which will be utilized in the job. Also the rest of code
-assumes such structure of allocated resources.
-
-Working directory has to be created before sending pbs job into the
-queue. Input file should be in working directory or full path to input
-file has to be specified. >Input file has to be defined by common
-CFX def file which is attached to the cfx solver via parameter
--def
-
-License**Â should be selected by parameter -P (Big letter **P**).
-Licensed products are the following: aa_r
-(ANSYS **Academic Research), ane3fl (ANSYS
-Multiphysics)-**Commercial.
-[ More
- about licensing
-here](licensing.html)
-
-Â
-
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-ANSYS Fluent
-============
-
-[ANSYS
-Fluent](http://www.ansys.com/Products/Simulation+Technology/Fluid+Dynamics/Fluid+Dynamics+Products/ANSYS+Fluent)
-software contains the broad physical modeling capabilities needed to
-model flow, turbulence, heat transfer, and reactions for industrial
-applications ranging from air flow over an aircraft wing to combustion
-in a furnace, from bubble columns to oil platforms, from blood flow to
-semiconductor manufacturing, and from clean room design to wastewater
-treatment plants. Special models that give the software the ability to
-model in-cylinder combustion, aeroacoustics, turbomachinery, and
-multiphase systems have served to broaden its reach.
-
-1. Common way to run Fluent over pbs file
-------------------------------------------------------
-
-To run ANSYS Fluent in batch mode you can utilize/modify the
-default fluent.pbs script and execute it via the qsub command.
-
-`
-#!/bin/bash
-#PBS -S /bin/bash
-#PBS -l nodes=2:ppn=16
-#PBS -q qprod
-#PBS -N $USER-Fluent-Project
-#PBS -A XX-YY-ZZ
-
-#! Mail to user when job terminate or abort
-#PBS -m ae
-
-#!change the working directory (default is home directory)
-#cd <working directory> (working directory must exists)
-WORK_DIR="/scratch/$USER/work"
-cd $WORK_DIR
-
-echo Running on host `hostname`
-echo Time is `date`
-echo Directory is `pwd`
-echo This jobs runs on the following processors:
-echo `cat $PBS_NODEFILE`
-
-#### Load ansys module so that we find the cfx5solve command
-module load ansys
-
-# Use following line to specify MPI for message-passing instead
-NCORES=`wc -l $PBS_NODEFILE |awk '{print $1}'`
-
-/ansys_inc/v145/fluent/bin/fluent 3d -t$NCORES -cnf=$PBS_NODEFILE -g -i fluent.jou
-`
-
-Header of the pbs file (above) is common and description can be find
-on [this
-site](../../resource-allocation-and-job-execution/job-submission-and-execution.html).
-[SVS FEM](http://www.svsfem.cz) recommends to utilize
-sources by keywords: nodes, ppn. These keywords allows to address
-directly the number of nodes (computers) and cores (ppn) which will be
-utilized in the job. Also the rest of code assumes such structure of
-allocated resources.
-
-Working directory has to be created before sending pbs job into the
-queue. Input file should be in working directory or full path to input
-file has to be specified. Input file has to be defined by common Fluent
-journal file which is attached to the Fluent solver via parameter -i
-fluent.jou
-
-Journal file with definition of the input geometry and boundary
-conditions and defined process of solution has e.g. the following
-structure:
-
- /file/read-case aircraft_2m.cas.gz
- /solve/init
- init
- /solve/iterate
- 10
- /file/write-case-dat aircraft_2m-solution
- /exit yes
-
-The appropriate dimension of the problem has to be set by
-parameter (2d/3d).Â
-
-2. Fast way to run Fluent from command line
---------------------------------------------------------
-
-`
-fluent solver_version [FLUENT_options] -i journal_file -pbs
-`
-
-This syntax will start the ANSYS FLUENT job under PBS Professional using
-the qsub command in a batch manner. When
-resources are available, PBS Professional will start the job and return
-a job ID, usually in the form of
-*job_ID.hostname*. This job ID can then be used
-to query, control, or stop the job using standard PBS Professional
-commands, such as qstat or
-qdel. The job will be run out of the current
-working directory, and all output will be written to the file
-fluent.o>Â
-*job_ID*. Â Â Â Â
-
-3. Running Fluent via user's config file
-----------------------------------------
-
-The sample script uses a configuration file called
-pbs_fluent.conf  if no command line arguments
-are present. This configuration file should be present in the directory
-from which the jobs are submitted (which is also the directory in which
-the jobs are executed). The following is an example of what the content
-of pbs_fluent.conf can be:
-
-`
-input="example_small.flin"
-case="Small-1.65m.cas"
-fluent_args="3d -pmyrinet"
-outfile="fluent_test.out"
-mpp="true"
-`
-
-The following is an explanation of the parameters:
-
- input is the name of the input
-file.
-
- case is the name of the
-.cas file that the input file will utilize.
-
- fluent_args are extra ANSYS FLUENT
-arguments. As shown in the previous example, you can specify the
-interconnect by using the -p interconnect
-command. The available interconnects include
-ethernet (the default),
-myrinet, class="monospace">
-infiniband, vendor,
-altix>, and
-crayx. The MPI is selected automatically, based
-on the specified interconnect.
-
- outfile is the name of the file to which
-the standard output will be sent.
-
- mpp="true" will tell the job script to
-execute the job across multiple processors. Â Â Â Â Â Â Â Â
-
-To run ANSYS Fluent in batch mode with user's config file you can
-utilize/modify the following script and execute it via the qsub
-command.
-
-`
-#!/bin/sh
-#PBS -l nodes=2:ppn=4
-#PBS -1 qprod
-#PBS -N $USE-Fluent-Project
-#PBS -A XX-YY-ZZ
-
- cd $PBS_O_WORKDIR
-
- #We assume that if they didn’t specify arguments then they should use the
- #config file if [ "xx${input}${case}${mpp}${fluent_args}zz" = "xxzz" ]; then
- if [ -f pbs_fluent.conf ]; then
- . pbs_fluent.conf
- else
- printf "No command line arguments specified, "
- printf "and no configuration file found. Exiting n"
- fi
- fi
-
-
- #Augment the ANSYS FLUENT command line arguments case "$mpp" in
- true)
- #MPI job execution scenario
- num_nodes=â€cat $PBS_NODEFILE | sort -u | wc -lâ€
- cpus=â€expr $num_nodes * $NCPUSâ€
- #Default arguments for mpp jobs, these should be changed to suit your
- #needs.
- fluent_args="-t${cpus} $fluent_args -cnf=$PBS_NODEFILE"
- ;;
- *)
- #SMP case
- #Default arguments for smp jobs, should be adjusted to suit your
- #needs.
- fluent_args="-t$NCPUS $fluent_args"
- ;;
- esac
- #Default arguments for all jobs
- fluent_args="-ssh -g -i $input $fluent_args"
-
- echo "---------- Going to start a fluent job with the following settings:
- Input: $input
- Case: $case
- Output: $outfile
- Fluent arguments: $fluent_args"
-
- #run the solver
- /ansys_inc/v145/fluent/bin/fluent $fluent_args > $outfile
-`
-
-It runs the jobs out of the directory from which they are
-submitted (PBS_O_WORKDIR).
-
-4. Running Fluent in parralel
------------------------------
-
-Fluent could be run in parallel only under Academic Research license. To
-do so this ANSYS Academic Research license must be placed before ANSYS
-CFD license in user preferences. To make this change anslic_admin
-utility should be run
-
-`
-/ansys_inc/shared_les/licensing/lic_admin/anslic_admin
-`
-
-ANSLIC_ADMIN Utility will be run
-
-
-
-
-
-
-
-Â
-
-ANSYS Academic Research license should be moved up to the top of the
-list.
-
-Â
-
-
-
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deleted file mode 100644
index 397aa94adba08eb00f01dec704c3031739e8d110..0000000000000000000000000000000000000000
--- a/converted/docs.it4i.cz/anselm-cluster-documentation/software/ansys/ansys-ls-dyna.md
+++ /dev/null
@@ -1,86 +0,0 @@
-ANSYS LS-DYNA
-=============
-
-[ANSYS
-LS-DYNA](http://www.ansys.com/Products/Simulation+Technology/Structural+Mechanics/Explicit+Dynamics/ANSYS+LS-DYNA)
-software provides convenient and easy-to-use access to the
-technology-rich, time-tested explicit solver without the need to contend
-with the complex input requirements of this sophisticated program.
-Introduced in 1996, ANSYS LS-DYNA capabilities have helped customers in
-numerous industries to resolve highly intricate design
-issues. >ANSYS Mechanical users have been able take advantage of
-complex explicit solutions for a long time utilizing the traditional
-ANSYS Parametric Design Language (APDL) environment. >These
-explicit capabilities are available to ANSYS Workbench users as well.
-The Workbench platform is a powerful, comprehensive, easy-to-use
-environment for engineering simulation. CAD import from all sources,
-geometry cleanup, automatic meshing, solution, parametric optimization,
-result visualization and comprehensive report generation are all
-available within a single fully interactive modern graphical user
-environment.
-
-To run ANSYS LS-DYNA in batch mode you can utilize/modify the
-default ansysdyna.pbs script and execute it via the qsub command.
-
-`
-#!/bin/bash
-#PBS -l nodes=2:ppn=16
-#PBS -q qprod
-#PBS -N $USER-DYNA-Project
-#PBS -A XX-YY-ZZ
-
-#! Mail to user when job terminate or abort
-#PBS -m ae
-
-#!change the working directory (default is home directory)
-#cd <working directory>
-WORK_DIR="/scratch/$USER/work"
-cd $WORK_DIR
-
-echo Running on host `hostname`
-echo Time is `date`
-echo Directory is `pwd`
-echo This jobs runs on the following processors:
-echo `cat $PBS_NODEFILE`
-
-#! Counts the number of processors
-NPROCS=`wc -l < $PBS_NODEFILE`
-
-echo This job has allocated $NPROCS nodes
-
-module load ansys
-
-#### Set number of processors per host listing
-#### (set to 1 as $PBS_NODEFILE lists each node twice if :ppn=2)
-procs_per_host=1
-#### Create host list
-hl=""
-for host in `cat $PBS_NODEFILE`
-do
- if [ "$hl" = "" ]
- then hl="$host:$procs_per_host"
- else hl="${hl}:$host:$procs_per_host"
- fi
-done
-
-echo Machines: $hl
-
-/ansys_inc/v145/ansys/bin/ansys145 -dis -lsdynampp i=input.k -machines $hl
-`
-
-Header of the pbs file (above) is common and description can be
-find on [this
-site](../../resource-allocation-and-job-execution/job-submission-and-execution.html)>.
-[SVS FEM](http://www.svsfem.cz) recommends to utilize
-sources by keywords: nodes, ppn. These keywords allows to address
-directly the number of nodes (computers) and cores (ppn) which will be
-utilized in the job. Also the rest of code assumes such structure of
-allocated resources.
-
-Working directory has to be created before sending pbs job into the
-queue. Input file should be in working directory or full path to input
-file has to be specified. Input file has to be defined by common LS-DYNA
-.**k** file which is attached to the ansys solver via parameter i=
-
-Â
-
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--- a/converted/docs.it4i.cz/anselm-cluster-documentation/software/ansys/ansys-mechanical-apdl.md
+++ /dev/null
@@ -1,81 +0,0 @@
-ANSYS MAPDL
-===========
-
-**[ANSYS
-Multiphysics](http://www.ansys.com/Products/Simulation+Technology/Structural+Mechanics/ANSYS+Multiphysics)**
-software offers a comprehensive product solution for both multiphysics
-and single-physics analysis. The product includes structural, thermal,
-fluid and both high- and low-frequency electromagnetic analysis. The
-product also contains solutions for both direct and sequentially coupled
-physics problems including direct coupled-field elements and the ANSYS
-multi-field solver.
-
-To run ANSYS MAPDL in batch mode you can utilize/modify the
-default mapdl.pbs script and execute it via the qsub command.
-
-`
-#!/bin/bash
-#PBS -l nodes=2:ppn=16
-#PBS -q qprod
-#PBS -N $USER-ANSYS-Project
-#PBS -A XX-YY-ZZ
-
-#! Mail to user when job terminate or abort
-#PBS -m ae
-
-#!change the working directory (default is home directory)
-#cd <working directory> (working directory must exists)
-WORK_DIR="/scratch/$USER/work"
-cd $WORK_DIR
-
-echo Running on host `hostname`
-echo Time is `date`
-echo Directory is `pwd`
-echo This jobs runs on the following processors:
-echo `cat $PBS_NODEFILE`
-
-module load ansys
-
-#### Set number of processors per host listing
-#### (set to 1 as $PBS_NODEFILE lists each node twice if :ppn=2)
-procs_per_host=1
-#### Create host list
-hl=""
-for host in `cat $PBS_NODEFILE`
-do
- if [ "$hl" = "" ]
- then hl="$host:$procs_per_host"
- else hl="${hl}:$host:$procs_per_host"
- fi
-done
-
-echo Machines: $hl
-
-#-i input.dat includes the input of analysis in APDL format
-#-o file.out is output file from ansys where all text outputs will be redirected
-#-p the name of license feature (aa_r=ANSYS Academic Research, ane3fl=Multiphysics(commercial), aa_r_dy=Academic AUTODYN)
-/ansys_inc/v145/ansys/bin/ansys145 -b -dis -p aa_r -i input.dat -o file.out -machines $hl -dir $WORK_DIR
-`
-
-Header of the pbs file (above) is common and description can be find on
-[this
-site](../../resource-allocation-and-job-execution/job-submission-and-execution.html).
-[SVS FEM](http://www.svsfem.cz) recommends to utilize
-sources by keywords: nodes, ppn. These keywords allows to address
-directly the number of nodes (computers) and cores (ppn) which will be
-utilized in the job. Also the rest of code assumes such structure of
-allocated resources.
-
-Working directory has to be created before sending pbs job into the
-queue. Input file should be in working directory or full path to input
-file has to be specified. Input file has to be defined by common APDL
-file which is attached to the ansys solver via parameter -i
-
-License** should be selected by parameter -p. Licensed products are
-the following: aa_r (ANSYS **Academic Research), ane3fl (ANSYS
-Multiphysics)-**Commercial**, aa_r_dy (ANSYS **Academic
-AUTODYN)>
-[ More
- about licensing
-here](licensing.html)
-
diff --git a/converted/docs.it4i.cz/anselm-cluster-documentation/software/ansys/ansys-mechanical-apdl.pdf b/converted/docs.it4i.cz/anselm-cluster-documentation/software/ansys/ansys-mechanical-apdl.pdf
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--- a/converted/docs.it4i.cz/anselm-cluster-documentation/software/ansys/ls-dyna.md
+++ /dev/null
@@ -1,65 +0,0 @@
-LS-DYNA
-=======
-
-[LS-DYNA](http://www.lstc.com/) is a multi-purpose,
-explicit and implicit finite element program used to analyze the
-nonlinear dynamic response of structures. Its fully automated contact
-analysis capability, a wide range of constitutive models to simulate a
-whole range of engineering materials (steels, composites, foams,
-concrete, etc.), error-checking features and the high scalability have
-enabled users worldwide to solve successfully many complex
-problems. >Additionally LS-DYNA is extensively used to simulate
-impacts on structures from drop tests, underwater shock, explosions or
-high-velocity impacts. Explosive forming, process engineering, accident
-reconstruction, vehicle dynamics, thermal brake disc analysis or nuclear
-safety are further areas in the broad range of possible applications. In
-leading-edge research LS-DYNA is used to investigate the behaviour of
-materials like composites, ceramics, concrete, or wood. Moreover, it is
-used in biomechanics, human modelling, molecular structures, casting,
-forging, or virtual testing.
-
-Anselm provides **1 commercial license of LS-DYNA without HPC**
-support now.Â
-
-To run LS-DYNA in batch mode you can utilize/modify the
-default lsdyna.pbs script and execute it via the qsub
-command.
-
-`
-#!/bin/bash
-#PBS -l nodes=1:ppn=16
-#PBS -q qprod
-#PBS -N $USER-LSDYNA-Project
-#PBS -A XX-YY-ZZ
-
-#! Mail to user when job terminate or abort
-#PBS -m ae
-
-#!change the working directory (default is home directory)
-#cd <working directory> (working directory must exists)
-WORK_DIR="/scratch/$USER/work"
-cd $WORK_DIR
-
-echo Running on host `hostname`
-echo Time is `date`
-echo Directory is `pwd`
-
-module load lsdyna
-
-/apps/engineering/lsdyna/lsdyna700s i=input.k
-`
-
-Header of the pbs file (above) is common and description can be find
-on [this
-site](../../resource-allocation-and-job-execution/job-submission-and-execution.html).
-[SVS FEM](http://www.svsfem.cz) recommends to utilize
-sources by keywords: nodes, ppn. These keywords allows to address
-directly the number of nodes (computers) and cores (ppn) which will be
-utilized in the job. Also the rest of code assumes such structure of
-allocated resources.
-
-Working directory has to be created before sending pbs job into the
-queue. Input file should be in working directory or full path to input
-file has to be specified. Input file has to be defined by common LS-DYNA
-.k** file which is attached to the LS-DYNA solver via parameter i=
-
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--- a/converted/docs.it4i.cz/anselm-cluster-documentation/software/chemistry/molpro.md
+++ /dev/null
@@ -1,91 +0,0 @@
-Molpro
-======
-
-Molpro is a complete system of ab initio programs for molecular
-electronic structure calculations.
-
-About Molpro
-------------
-
-Molpro is a software package used for accurate ab-initio quantum
-chemistry calculations. More information can be found at the [official
-webpage](http://www.molpro.net/).
-
-License
--------
-
-Molpro software package is available only to users that have a valid
-license. Please contact support to enable access to Molpro if you have a
-valid license appropriate for running on our cluster (eg. >academic
-research group licence, parallel execution).
-
-To run Molpro, you need to have a valid license token present in
-" $HOME/.molpro/token". You can
-download the token from [Molpro
-website](https://www.molpro.net/licensee/?portal=licensee).
-
-Installed version
------------------
-
-Currently on Anselm is installed version 2010.1, patch level 45,
-parallel version compiled with Intel compilers and Intel MPI.
-
-Compilation parameters are default :
-
- |Parameter|Value|
- ------------------------------------------- |---|---|-------------------
- |max number of atoms|200|
- |max number of valence orbitals|300|
- |max number of basis functions|4095|
- |max number of states per symmmetry|20|
- |max number of state symmetries|16|
- |max number of records|200|
- |max number of primitives|maxbfn x [2]|
-
-Â
-
-Running
--------
-
-Molpro is compiled for parallel execution using MPI and OpenMP. By
-default, Molpro reads the number of allocated nodes from PBS and
-launches a data server on one node. On the remaining allocated nodes,
-compute processes are launched, one process per node, each with 16
-threads. You can modify this behavior by using -n, -t and helper-server
-options. Please refer to the [Molpro
-documentation](http://www.molpro.net/info/2010.1/doc/manual/node9.html)
-for more details.Â
-
-The OpenMP parallelization in Molpro is limited and has been observed to
-produce limited scaling. We therefore recommend to use MPI
-parallelization only. This can be achieved by passing option
-mpiprocs=16:ompthreads=1 to PBS.
-
-You are advised to use the -d option to point to a directory in [SCRATCH
-filesystem](../../storage.html). Molpro can produce a
-large amount of temporary data during its run, and it is important that
-these are placed in the fast scratch filesystem.
-
-### Example jobscript
-
- #PBS -A IT4I-0-0
- #PBS -q qprod
- #PBS -l select=1:ncpus=16:mpiprocs=16:ompthreads=1
-
- cd $PBS_O_WORKDIR
-
- # load Molpro module
- module add molpro
-
- # create a directory in the SCRATCH filesystem
- mkdir -p /scratch/$USER/$PBS_JOBID
-
- # copy an example input
- cp /apps/chem/molpro/2010.1/molprop_2010_1_Linux_x86_64_i8/examples/caffeine_opt_diis.com .
-
- # run Molpro with default options
- molpro -d /scratch/$USER/$PBS_JOBID caffeine_opt_diis.com
-
- # delete scratch directory
- rm -rf /scratch/$USER/$PBS_JOBIDÂ
-
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--- a/converted/docs.it4i.cz/anselm-cluster-documentation/software/chemistry/nwchem.md
+++ /dev/null
@@ -1,66 +0,0 @@
-NWChem
-======
-
-High-Performance Computational Chemistry
-
-Introduction
--------------------------
-
-NWChem aims to provide its users with computational chemistry
-tools that are scalable both in their ability to treat large scientific
-computational chemistry problems efficiently, and in their use of
-available parallel computing resources from high-performance parallel
-supercomputers to conventional workstation clusters.
-
-[Homepage](http://www.nwchem-sw.org/index.php/Main_Page)
-
-Installed versions
-------------------
-
-The following versions are currently installed :Â
-
-- 6.1.1, not recommended, problems have been observed with this
- version
-
-- 6.3-rev2-patch1, current release with QMD patch applied. Compiled
- with Intel compilers, MKL and Intel MPI
-
-- 6.3-rev2-patch1-openmpi, same as above, but compiled with OpenMPI
- and NWChem provided BLAS instead of MKL. This version is expected to
- be slower
-
-- 6.3-rev2-patch1-venus, this version contains only libraries for
- VENUS interface linking. Does not provide standalone NWChem
- executable
-
-For a current list of installed versions, execute :Â
-
- module avail nwchem
-
-Running
--------
-
-NWChem is compiled for parallel MPI execution. Normal procedure for MPI
-jobs applies. Sample jobscript :
-
- #PBS -A IT4I-0-0
- #PBS -q qprod
- #PBS -l select=1:ncpus=16
-
- module add nwchem/6.3-rev2-patch1
- mpirun -np 16 nwchem h2o.nw
-
-Options
---------------------
-
-Please refer to [the
-documentation](http://www.nwchem-sw.org/index.php/Release62:Top-level)Â and
-in the input file set the following directives :
-
-- >MEMORY : controls the amount of memory NWChem will use
-- >SCRATCH_DIR : set this to a directory in [SCRATCH
- filesystem](../../storage.html#scratch)Â (or run the
- calculation completely in a scratch directory). For certain
- calculations, it might be advisable to reduce I/O by forcing
- "direct" mode, eg. "scf direct"
-
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+++ /dev/null
@@ -1,163 +0,0 @@
-Compilers
-=========
-
-Available compilers, including GNU, INTEL and UPC compilers
-
-
-
-Currently there are several compilers for different programming
-languages available on the Anselm cluster:
-
-- C/C++
-- Fortran 77/90/95
-- Unified Parallel C
-- Java
-- nVidia CUDA
-
-Â
-
-The C/C++ and Fortran compilers are divided into two main groups GNU and
-Intel.
-
-Intel Compilers
----------------
-
-For information about the usage of Intel Compilers and other Intel
-products, please read the [Intel Parallel
-studio](intel-suite.html) page.
-
-GNU C/C++ and Fortran Compilers
--------------------------------
-
-For compatibility reasons there are still available the original (old
-4.4.6-4) versions of GNU compilers as part of the OS. These are
-accessible in the search path by default.
-
-It is strongly recommended to use the up to date version (4.8.1) which
-comes with the module gcc:
-
- $ module load gcc
- $ gcc -v
- $ g++ -v
- $ gfortran -v
-
-With the module loaded two environment variables are predefined. One for
-maximum optimizations on the Anselm cluster architecture, and the other
-for debugging purposes:
-
- $ echo $OPTFLAGS
- -O3 -march=corei7-avx
-
- $ echo $DEBUGFLAGS
- -O0 -g
-
-For more informations about the possibilities of the compilers, please
-see the man pages.
-
-Unified Parallel C
-------------------
-
-UPC is supported by two compiler/runtime implementations:
-
-- GNU - SMP/multi-threading support only
-- Berkley - multi-node support as well as SMP/multi-threading support
-
-### GNU UPC Compiler
-
-To use the GNU UPC compiler and run the compiled binaries use the module
-gupc
-
- $ module add gupc
- $ gupc -v
- $ g++ -v
-
-Simple program to test the compiler
-
- $ cat count.upc
-
- /* hello.upc - a simple UPC example */
- #include <upc.h>
- #include <stdio.h>
-
- int main() {
- Â if (MYTHREAD == 0) {
- Â Â Â printf("Welcome to GNU UPC!!!n");
- Â }
- Â upc_barrier;
- Â printf(" - Hello from thread %in", MYTHREAD);
- Â return 0;
- }
-
-To compile the example use
-
- $ gupc -o count.upc.x count.upc
-
-To run the example with 5 threads issue
-
- $ ./count.upc.x -fupc-threads-5
-
-For more informations see the man pages.
-
-### Berkley UPC Compiler
-
-To use the Berkley UPC compiler and runtime environment to run the
-binaries use the module bupc
-
- $ module add bupc
- $ upcc -version
-
-As default UPC network the "smp" is used. This is very quick and easy
-way for testing/debugging, but limited to one node only.
-
-For production runs, it is recommended to use the native Infiband
-implementation of UPC network "ibv". For testing/debugging using
-multiple nodes, the "mpi" UPC network is recommended. Please note, that
-the selection of the network is done at the compile time** and not at
-runtime (as expected)!
-
-Example UPC code:
-
- $ cat hello.upc
-
- /* hello.upc - a simple UPC example */
- #include <upc.h>
- #include <stdio.h>
-
- int main() {
- Â if (MYTHREAD == 0) {
- Â Â Â printf("Welcome to Berkeley UPC!!!n");
- Â }
- Â upc_barrier;
- Â printf(" - Hello from thread %in", MYTHREAD);
- Â return 0;
- }
-
-To compile the example with the "ibv" UPC network use
-
- $ upcc -network=ibv -o hello.upc.x hello.upc
-
-To run the example with 5 threads issue
-
- $ upcrun -n 5 ./hello.upc.x
-
-To run the example on two compute nodes using all 32 cores, with 32
-threads, issue
-
- $ qsub -I -q qprod -A PROJECT_ID -l select=2:ncpus=16
- $ module add bupc
- $ upcrun -n 32 ./hello.upc.x
-
-Â For more informations see the man pages.
-
-Java
-----
-
-For information how to use Java (runtime and/or compiler), please read
-the [Java page](java.html).
-
-nVidia CUDA
------------
-
-For information how to work with nVidia CUDA, please read the [nVidia
-CUDA page](nvidia-cuda.html).
-
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--- a/converted/docs.it4i.cz/anselm-cluster-documentation/software/comsol/comsol-multiphysics.md
+++ /dev/null
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-COMSOL Multiphysics®
-====================
-
-
-
-Introduction
-
--------------------------
-
-[COMSOL](http://www.comsol.com)
-is a powerful environment for modelling and solving various engineering
-and scientific problems based on partial differential equations. COMSOL
-is designed to solve coupled or multiphysics phenomena. For many
-standard engineering problems COMSOL provides add-on products such as
-electrical, mechanical, fluid flow, and chemical
-applications.
-
-- >[Structural Mechanics
- Module](http://www.comsol.com/structural-mechanics-module),
-
-
-- >[Heat Transfer
- Module](http://www.comsol.com/heat-transfer-module),
-
-
-- >[CFD
- Module](http://www.comsol.com/cfd-module),
-
-
-- >[Acoustics
- Module](http://www.comsol.com/acoustics-module),
-
-
-- >and [many
- others](http://www.comsol.com/products)
-
-COMSOL also allows an
-interface support for
-equation-based modelling of
-partial differential
-equations.
-
-Execution
-
-----------------------
-
-On the Anselm cluster COMSOL is available in the latest
-stable version. There are two variants of the release:
-
-- >**Non commercial** or so
- called >**EDU
- variant**>, which can be used for research
- and educational purposes.
-
-- >**Commercial** or so called
- >**COM variant**,
- which can used also for commercial activities.
- >**COM variant**
- has only subset of features compared to the
- >**EDU
- variant**> available.
-
- More about
- licensing will be posted here
- soon.
-
-
-To load the of COMSOL load the module
-
-`
-$ module load comsol
-`
-
-By default the **EDU
-variant**> will be loaded. If user needs other
-version or variant, load the particular version. To obtain the list of
-available versions use
-
-`
-$ module avail comsol
-`
-
-If user needs to prepare COMSOL jobs in the interactive mode
-it is recommend to use COMSOL on the compute nodes via PBS Pro
-scheduler. In order run the COMSOL Desktop GUI on Windows is recommended
-to use the [Virtual Network Computing
-(VNC)](https://docs.it4i.cz/anselm-cluster-documentation/software/comsol/resolveuid/11e53ad0d2fd4c5187537f4baeedff33).
-
-`
-$ xhost +
-$ qsub -I -X -A PROJECT_ID -q qprod -l select=1:ncpus=16
-$ module load comsol
-$ comsol
-`
-
-To run COMSOL in batch mode, without the COMSOL Desktop GUI
-environment, user can utilized the default (comsol.pbs) job script and
-execute it via the qsub command.
-
-`
-#!/bin/bash
-#PBS -l select=3:ncpus=16
-#PBS -q qprod
-#PBS -N JOB_NAME
-#PBS -AÂ PROJECT_ID
-
-cd /scratch/$USER/ || exit
-
-echo Time is `date`
-echo Directory is `pwd`
-echo '**PBS_NODEFILE***START*******'
-cat $PBS_NODEFILE
-echo '**PBS_NODEFILE***END*********'
-
-text_nodes < cat $PBS_NODEFILE
-
-module load comsol
-# module load comsol/43b-COM
-
-ntask=$(wc -l $PBS_NODEFILE)
-
-comsol -nn ${ntask} batch -configuration /tmp –mpiarg –rmk –mpiarg pbs -tmpdir /scratch/$USER/ -inputfile name_input_f.mph -outputfile name_output_f.mph -batchlog name_log_f.log
-`
-
-Working directory has to be created before sending the
-(comsol.pbs) job script into the queue. Input file (name_input_f.mph)
-has to be in working directory or full path to input file has to be
-specified. The appropriate path to the temp directory of the job has to
-be set by command option (-tmpdir).
-
-LiveLink™* *for MATLAB®^
--------------------------
-
-COMSOL is the software package for the numerical solution of
-the partial differential equations. LiveLink for MATLAB allows
-connection to the
-COMSOL>><span><span><span><span>**®**</span>^
-API (Application Programming Interface) with the benefits of the
-programming language and computing environment of the MATLAB.
-
-LiveLink for MATLAB is available in both
-**EDU** and
-**COM**
-**variant** of the
-COMSOL release. On Anselm 1 commercial
-(>**COM**) license
-and the 5 educational
-(>**EDU**) licenses
-of LiveLink for MATLAB (please see the [ISV
-Licenses](../isv_licenses.html)) are available.
-Following example shows how to start COMSOL model from MATLAB via
-LiveLink in the interactive mode.
-
-`
-$ xhost +
-$ qsub -I -X -A PROJECT_ID -q qexp -l select=1:ncpus=16
-$ module load matlab
-$ module load comsol
-$ comsol server matlab
-`
-
-At the first time to launch the LiveLink for MATLAB
-(client-MATLAB/server-COMSOL connection) the login and password is
-requested and this information is not requested again.
-
-To run LiveLink for MATLAB in batch mode with
-(comsol_matlab.pbs) job script you can utilize/modify the following
-script and execute it via the qsub command.
-
-`
-#!/bin/bash
-#PBS -l select=3:ncpus=16
-#PBS -q qprod
-#PBS -N JOB_NAME
-#PBS -AÂ PROJECT_ID
-
-cd /scratch/$USER || exit
-
-echo Time is `date`
-echo Directory is `pwd`
-echo '**PBS_NODEFILE***START*******'
-cat $PBS_NODEFILE
-echo '**PBS_NODEFILE***END*********'
-
-text_nodes < cat $PBS_NODEFILE
-
-module load matlab
-module load comsol/43b-EDU
-
-ntask=$(wc -l $PBS_NODEFILE)
-
-comsol -nn ${ntask} server -configuration /tmp -mpiarg -rmk -mpiarg pbs -tmpdir /scratch/$USER &
-cd /apps/engineering/comsol/comsol43b/mli
-matlab -nodesktop -nosplash -r "mphstart; addpath /scratch/$USER; test_job"
-`
-
-This example shows how to run Livelink for MATLAB with following
-configuration: 3 nodes and 16 cores per node. Working directory has to
-be created before submitting (comsol_matlab.pbs) job script into the
-queue. Input file (test_job.m) has to be in working directory or full
-path to input file has to be specified. The Matlab command option (-r
-”mphstart”) created a connection with a COMSOL server using the default
-port number.
-
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-Debuggers and profilers summary
-===============================
-
-
-
-Introduction
-------------
-
-We provide state of the art programms and tools to develop, profile and
-debug HPC codes at IT4Innovations.
-On these pages, we provide an overview of the profiling and debugging
-tools available on Anslem at IT4I.
-
-Intel debugger
---------------
-
-The intel debugger version 13.0 is available, via module intel. The
-debugger works for applications compiled with C and C++ compiler and the
-ifort fortran 77/90/95 compiler. The debugger provides java GUI
-environment. Use [X
-display](https://docs.it4i.cz/anselm-cluster-documentation/software/debuggers/resolveuid/11e53ad0d2fd4c5187537f4baeedff33)
-for running the GUI.
-
- $ module load intel
- $ idb
-
-Read more at the [Intel
-Debugger](intel-suite/intel-debugger.html) page.
-
-Allinea Forge (DDT/MAP)
------------------------
-
-Allinea DDT, is a commercial debugger primarily for debugging parallel
-MPI or OpenMP programs. It also has a support for GPU (CUDA) and Intel
-Xeon Phi accelerators. DDT provides all the standard debugging features
-(stack trace, breakpoints, watches, view variables, threads etc.) for
-every thread running as part of your program, or for every process -
-even if these processes are distributed across a cluster using an MPI
-implementation.
-
- $ module load Forge
- $ forge
-
-Read more at the [Allinea
-DDT](debuggers/allinea-ddt.html) page.
-
-Allinea Performance Reports
----------------------------
-
-Allinea Performance Reports characterize the performance of HPC
-application runs. After executing your application through the tool, a
-synthetic HTML report is generated automatically, containing information
-about several metrics along with clear behavior statements and hints to
-help you improve the efficiency of your runs. Our license is limited to
-64 MPI processes.
-
- $ module load PerformanceReports/6.0
- $ perf-report mpirun -n 64 ./my_application argument01 argument02
-
-Read more at the [Allinea Performance
-Reports](debuggers/allinea-performance-reports.html)
-page.
-
-RougeWave Totalview
--------------------
-
-TotalView is a source- and machine-level debugger for multi-process,
-multi-threaded programs. Its wide range of tools provides ways to
-analyze, organize, and test programs, making it easy to isolate and
-identify problems in individual threads and processes in programs of
-great complexity.
-
- $ module load totalview
- $ totalview
-
-Read more at the [Totalview](debuggers/total-view.html)
-page.
-
-Vampir trace analyzer
----------------------
-
-Vampir is a GUI trace analyzer for traces in OTF format.
-
- $ module load Vampir/8.5.0
- $ vampir
-
-Read more at
-the [Vampir](../../salomon/software/debuggers/vampir.html) page.
-
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-Allinea Forge (DDT,MAP)
-=======================
-
-
-
-Allinea Forge consist of two tools - debugger DDT and profiler MAP.
-
-Allinea DDT, is a commercial debugger primarily for debugging parallel
-MPI or OpenMP programs. It also has a support for GPU (CUDA) and Intel
-Xeon Phi accelerators. DDT provides all the standard debugging features
-(stack trace, breakpoints, watches, view variables, threads etc.) for
-every thread running as part of your program, or for every process -
-even if these processes are distributed across a cluster using an MPI
-implementation.
-
-Allinea MAP is a profiler for C/C++/Fortran HPC codes. It is designed
-for profiling parallel code, which uses pthreads, OpenMP or MPI.
-
-License and Limitations for Anselm Users
-----------------------------------------
-
-On Anselm users can debug OpenMP or MPI code that runs up to 64 parallel
-processes. In case of debugging GPU or Xeon Phi accelerated codes the
-limit is 8 accelerators. These limitation means that:
-
-- 1 user can debug up 64 processes, or
-- 32 users can debug 2 processes, etc.
-
-In case of debugging on accelerators:
-
-- 1 user can debug on up to 8 accelerators, orÂ
-- 8 users can debug on single accelerator.Â
-
-Compiling Code to run with DDT
-------------------------------
-
-### Modules
-
-Load all necessary modules to compile the code. For example:Â
-
- $ module load intel
- $ module load impi ... or ... module load openmpi/X.X.X-icc
-
-Load the Allinea DDT module:
-
- $ module load Forge
-
-Compile the code:
-
-`
-$ mpicc -g -O0 -o test_debug test.c
-
-$ mpif90 -g -O0 -o test_debug test.f
-`
-
-Â
-
-### Compiler flags
-
-Before debugging, you need to compile your code with theses flags:
-
--g** : Generates extra debugging information usable by GDB. -g3**
-includes even more debugging information. This option is available for
-GNU and INTEL C/C++ and Fortran compilers.
-
--O0** : Suppress all optimizations.**
-
-Â
-
-Starting a Job with DDT
------------------------
-
-Be sure to log in with an X window
-forwarding enabled. This could mean using the -X in the ssh: Â
-
- $ ssh -X username@anselm.it4i.cz
-
-Other options is to access login node using VNC. Please see the detailed
-information on how to [use graphic user interface on
-Anselm](https://docs.it4i.cz/anselm-cluster-documentation/software/debuggers/resolveuid/11e53ad0d2fd4c5187537f4baeedff33)
-.
-
-From the login node an interactive session **with X windows forwarding**
-(-X option) can be started by following command:Â
-
- $ qsub -I -X -A NONE-0-0 -q qexp -lselect=1:ncpus=16:mpiprocs=16,walltime=01:00:00Â
-
-Then launch the debugger with the ddt command followed by the name of
-the executable to debug:
-
- $ ddt test_debug
-
-A submission window that appears have
-a prefilled path to the executable to debug. You can select the number
-of MPI processors and/or OpenMP threads on which to run and press run.
-Command line arguments to a program can be entered to the
-"Arguments "
-box.
-
-Â
-
-To start the debugging directly without the submission window, user can
-specify the debugging and execution parameters from the command line.
-For example the number of MPI processes is set by option "-np 4".
-Skipping the dialog is done by "-start" option. To see the list of the
-"ddt" command line parameters, run "ddt --help". Â
-
- ddt -start -np 4 ./hello_debug_impi
-
-Â
-
-Documentation
--------------
-
-Users can find original User Guide after loading the DDT module:Â
-
- $DDTPATH/doc/userguide.pdf
-
-Â
-
-Â
-
-Â [1] Discipline, Magic, Inspiration and Science: Best Practice
-Debugging with Allinea DDT, Workshop conducted at LLNL by Allinea on May
-10, 2013,
-[link](https://computing.llnl.gov/tutorials/allineaDDT/index.html)
-
-Â
-
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-Allinea Performance Reports
-===========================
-
-quick application profiling
-
-
-
-Introduction
-------------
-
-Allinea Performance Reports characterize the performance of HPC
-application runs. After executing your application through the tool, a
-synthetic HTML report is generated automatically, containing information
-about several metrics along with clear behavior statements and hints to
-help you improve the efficiency of your runs.
-
-The Allinea Performance Reports is most useful in profiling MPI
-programs.
-
-Our license is limited to 64 MPI processes.
-
-Modules
--------
-
-Allinea Performance Reports version 6.0 is available
-
- $ module load PerformanceReports/6.0
-
-The module sets up environment variables, required for using the Allinea
-Performance Reports. This particular command loads the default module,
-which is performance reports version 4.2.
-
-Usage
------
-
-Use the the perf-report wrapper on your (MPI) program.
-
-Instead of [running your MPI program the usual
-way](../mpi-1.html), use the the perf report wrapper:
-
- $ perf-report mpirun ./mympiprog.x
-
-The mpi program will run as usual. The perf-report creates two
-additional files, in *.txt and *.html format, containing the
-performance report. Note that [demanding MPI codes should be run within
-the queue
-system](../../resource-allocation-and-job-execution/job-submission-and-execution.html).
-
-Example
--------
-
-In this example, we will be profiling the mympiprog.x MPI program, using
-Allinea performance reports. Assume that the code is compiled with intel
-compilers and linked against intel MPI library:
-
-First, we allocate some nodes via the express queue:
-
- $ qsub -q qexp -l select=2:ncpus=16:mpiprocs=16:ompthreads=1 -I
- qsub: waiting for job 262197.dm2 to start
- qsub: job 262197.dm2 ready
-
-Then we load the modules and run the program the usual way:
-
- $ module load intel impi allinea-perf-report/4.2
- $ mpirun ./mympiprog.x
-
-Now lets profile the code:
-
- $ perf-report mpirun ./mympiprog.x
-
-Performance report files
-[mympiprog_32p*.txt](mympiprog_32p_2014-10-15_16-56.txt)
-and
-[mympiprog_32p*.html](mympiprog_32p_2014-10-15_16-56.html)
-were created. We can see that the code is very efficient on MPI and is
-CPU bounded.
-
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-CUBE
-====
-
-Introduction
-------------
-
-CUBE is a graphical performance report explorer for displaying data from
-Score-P and Scalasca (and other compatible tools). The name comes from
-the fact that it displays performance data in a three-dimensions :
-
-- **performance metric**, where a number of metrics are available,
- such as communication time or cache misses,
-- **call path**, which contains the call tree of your program
-- s**ystem resource**, which contains system's nodes, processes and
- threads, depending on the parallel programming model.
-
-Each dimension is organized in a tree, for example the time performance
-metric is divided into Execution time and Overhead time, call path
-dimension is organized by files and routines in your source code etc.
-
-
-
-*Figure 1. Screenshot of CUBE displaying data from Scalasca.*
-
-*
-*Each node in the tree is colored by severity (the color scheme is
-displayed at the bottom of the window, ranging from the least severe
-blue to the most severe being red). For example in Figure 1, we can see
-that most of the point-to-point MPI communication happens in routine
-exch_qbc, colored red.
-
-Installed versions
-------------------
-
-Currently, there are two versions of CUBE 4.2.3 available as
-[modules](../../environment-and-modules.html) :
-
-- class="s1"> cube/4.2.3-gcc,
- compiled with GCC
-
-- class="s1"> cube/4.2.3-icc,
- compiled with Intel compiler
-
-Usage
------
-
-CUBE is a graphical application. Refer to [Graphical User Interface
-documentation](https://docs.it4i.cz/anselm-cluster-documentation/software/debuggers/resolveuid/11e53ad0d2fd4c5187537f4baeedff33)
-for a list of methods to launch graphical applications on Anselm.
-
-Analyzing large data sets can consume large amount of CPU and RAM. Do
-not perform large analysis on login nodes.
-
-After loading the apropriate module, simply launch
-cube command, or alternatively you can use
- scalasca -examine command to launch the
-GUI. Note that for Scalasca datasets, if you do not analyze the data
-with > scalasca
--examine before to opening them with CUBE, not all
-performance data will be available.
-
-Â >References
-
-1. <http://www.scalasca.org/software/cube-4.x/download.html>
-
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-Intel Performance Counter Monitor
-=================================
-
-Introduction
-------------
-
-Intel PCM (Performance Counter Monitor) is a tool to monitor performance
-hardware counters on Intel>® processors, similar to
-[PAPI](papi.html). The difference between PCM and PAPI
-is that PCM supports only Intel hardware, but PCM can monitor also
-uncore metrics, like memory controllers and >QuickPath Interconnect
-links.
-
-Installed version
-------------------------------
-
-Currently installed version 2.6. To load the
-[module](../../environment-and-modules.html), issue :
-
- $ module load intelpcm
-
-Command line tools
-------------------
-
-PCM provides a set of tools to monitor system/or application.Â
-
-### pcm-memory
-
-Measures memory bandwidth of your application or the whole system.
-Usage:
-
- $ pcm-memory.x <delay>|[external_program parameters]
-
-Specify either a delay of updates in seconds or an external program to
-monitor. If you get an error about PMU in use, respond "y" and relaunch
-the program.
-
-Sample output:
-
- ---------------------------------------||---------------------------------------
- -- Socket 0 --||-- Socket 1 --
- ---------------------------------------||---------------------------------------
- ---------------------------------------||---------------------------------------
- ---------------------------------------||---------------------------------------
- -- Memory Performance Monitoring --||-- Memory Performance Monitoring --
- ---------------------------------------||---------------------------------------
- -- Mem Ch 0: Reads (MB/s): 2.44 --||-- Mem Ch 0: Reads (MB/s): 0.26 --
- -- Writes(MB/s): 2.16 --||-- Writes(MB/s): 0.08 --
- -- Mem Ch 1: Reads (MB/s): 0.35 --||-- Mem Ch 1: Reads (MB/s): 0.78 --
- -- Writes(MB/s): 0.13 --||-- Writes(MB/s): 0.65 --
- -- Mem Ch 2: Reads (MB/s): 0.32 --||-- Mem Ch 2: Reads (MB/s): 0.21 --
- -- Writes(MB/s): 0.12 --||-- Writes(MB/s): 0.07 --
- -- Mem Ch 3: Reads (MB/s): 0.36 --||-- Mem Ch 3: Reads (MB/s): 0.20 --
- -- Writes(MB/s): 0.13 --||-- Writes(MB/s): 0.07 --
- -- NODE0 Mem Read (MB/s): 3.47 --||-- NODE1 Mem Read (MB/s): 1.45 --
- -- NODE0 Mem Write (MB/s): 2.55 --||-- NODE1 Mem Write (MB/s): 0.88 --
- -- NODE0 P. Write (T/s) : 31506 --||-- NODE1 P. Write (T/s): 9099 --
- -- NODE0 Memory (MB/s): 6.02 --||-- NODE1 Memory (MB/s): 2.33 --
- ---------------------------------------||---------------------------------------
- -- System Read Throughput(MB/s): 4.93 --
- -- System Write Throughput(MB/s): 3.43 --
- -- System Memory Throughput(MB/s): 8.35 --
- ---------------------------------------||---------------------------------------Â
-
-### pcm-msr
-
-Command pcm-msr.x can be used to
-read/write model specific registers of the CPU.
-
-### pcm-numa
-
-NUMA monitoring utility does not work on Anselm.
-
-### pcm-pcie
-
-Can be used to monitor PCI Express bandwith. Usage:
-pcm-pcie.x <delay>
-
-### pcm-power
-
-Displays energy usage and thermal headroom for CPU and DRAM sockets.
-Usage:Â >Â pcm-power.x <delay> |
-<external program>
-
-### pcm
-
-This command provides an overview of performance counters and memory
-usage. >Usage: > pcm.x
-<delay> | <external program>
-
-Sample output :
-
- $ pcm.x ./matrix
-
- Intel(r) Performance Counter Monitor V2.6 (2013-11-04 13:43:31 +0100 ID=db05e43)
-
- Copyright (c) 2009-2013 Intel Corporation
-
- Number of physical cores: 16
- Number of logical cores: 16
- Threads (logical cores) per physical core: 1
- Num sockets: 2
- Core PMU (perfmon) version: 3
- Number of core PMU generic (programmable) counters: 8
- Width of generic (programmable) counters: 48 bits
- Number of core PMU fixed counters: 3
- Width of fixed counters: 48 bits
- Nominal core frequency: 2400000000 Hz
- Package thermal spec power: 115 Watt; Package minimum power: 51 Watt; Package maximum power: 180 Watt;
- Socket 0: 1 memory controllers detected with total number of 4 channels. 2 QPI ports detected.
- Socket 1: 1 memory controllers detected with total number of 4 channels. 2 QPI ports detected.
- Number of PCM instances: 2
- Max QPI link speed: 16.0 GBytes/second (8.0 GT/second)
-
- Detected Intel(R) Xeon(R) CPU E5-2665 0 @ 2.40GHz "Intel(r) microarchitecture codename Sandy Bridge-EP/Jaketown"
-
- Executing "./matrix" command:
-
- Exit code: 0
-
- EXEC : instructions per nominal CPU cycle
- IPC : instructions per CPU cycle
- FREQ : relation to nominal CPU frequency='unhalted clock ticks'/'invariant timer ticks' (includes Intel Turbo Boost)
- AFREQ : relation to nominal CPU frequency while in active state (not in power-saving C state)='unhalted clock ticks'/'invariant timer ticks while in C0-state' (includes Intel Turbo Boost)
- L3MISS: L3 cache misses
- L2MISS: L2 cache misses (including other core's L2 cache *hits*)
- L3HIT : L3 cache hit ratio (0.00-1.00)
- L2HIT : L2 cache hit ratio (0.00-1.00)
- L3CLK : ratio of CPU cycles lost due to L3 cache misses (0.00-1.00), in some cases could be >1.0 due to a higher memory latency
- L2CLK : ratio of CPU cycles lost due to missing L2 cache but still hitting L3 cache (0.00-1.00)
- READ : bytes read from memory controller (in GBytes)
- WRITE : bytes written to memory controller (in GBytes)
- TEMP : Temperature reading in 1 degree Celsius relative to the TjMax temperature (thermal headroom): 0 corresponds to the max temperature
-
- Core (SKT) | EXEC | IPC | FREQ | AFREQ | L3MISS | L2MISS | L3HIT | L2HIT | L3CLK | L2CLK | READ | WRITE | TEMP
-
- 0 0 0.00 0.64 0.01 0.80 5592 11 K 0.49 0.13 0.32 0.06 N/A N/A 67
- 1 0 0.00 0.18 0.00 0.69 3086 5552 0.44 0.07 0.48 0.08 N/A N/A 68
- 2 0 0.00 0.23 0.00 0.81 300 562 0.47 0.06 0.43 0.08 N/A N/A 67
- 3 0 0.00 0.21 0.00 0.99 437 862 0.49 0.06 0.44 0.09 N/A N/A 73
- 4 0 0.00 0.23 0.00 0.93 293 559 0.48 0.07 0.42 0.09 N/A N/A 73
- 5 0 0.00 0.21 0.00 1.00 423 849 0.50 0.06 0.43 0.10 N/A N/A 69
- 6 0 0.00 0.23 0.00 0.94 285 558 0.49 0.06 0.41 0.09 N/A N/A 71
- 7 0 0.00 0.18 0.00 0.81 674 1130 0.40 0.05 0.53 0.08 N/A N/A 65
- 8 1 0.00 0.47 0.01 1.26 6371 13 K 0.51 0.35 0.31 0.07 N/A N/A 64
- 9 1 2.30 1.80 1.28 1.29 179 K 15 M 0.99 0.59 0.04 0.71 N/A N/A 60
- 10 1 0.00 0.22 0.00 1.26 315 570 0.45 0.06 0.43 0.08 N/A N/A 67
- 11 1 0.00 0.23 0.00 0.74 321 579 0.45 0.05 0.45 0.07 N/A N/A 66
- 12 1 0.00 0.22 0.00 1.25 305 570 0.46 0.05 0.42 0.07 N/A N/A 68
- 13 1 0.00 0.22 0.00 1.26 336 581 0.42 0.04 0.44 0.06 N/A N/A 69
- 14 1 0.00 0.22 0.00 1.25 314 565 0.44 0.06 0.43 0.07 N/A N/A 69
- 15 1 0.00 0.29 0.00 1.19 2815 6926 0.59 0.39 0.29 0.08 N/A N/A 69
- -------------------------------------------------------------------------------------------------------------------
- SKT 0 0.00 0.46 0.00 0.79 11 K 21 K 0.47 0.10 0.38 0.07 0.00 0.00 65
- SKT 1 0.29 1.79 0.16 1.29 190 K 15 M 0.99 0.59 0.05 0.70 0.01 0.01 61
- -------------------------------------------------------------------------------------------------------------------
- TOTAL * 0.14 1.78 0.08 1.28 201 K 15 M 0.99 0.59 0.05 0.70 0.01 0.01 N/A
-
- Instructions retired: 1345 M ; Active cycles: 755 M ; Time (TSC): 582 Mticks ; C0 (active,non-halted) core residency: 6.30 %
-
- C1 core residency: 0.14 %; C3 core residency: 0.20 %; C6 core residency: 0.00 %; C7 core residency: 93.36 %;
- C2 package residency: 48.81 %; C3 package residency: 0.00 %; C6 package residency: 0.00 %; C7 package residency: 0.00 %;
-
- PHYSICAL CORE IPC : 1.78 => corresponds to 44.50 % utilization for cores in active state
- Instructions per nominal CPU cycle: 0.14 => corresponds to 3.60 % core utilization over time interval
-
- Intel(r) QPI data traffic estimation in bytes (data traffic coming to CPU/socket through QPI links):
-
- QPI0 QPI1 | QPI0 QPI1
- ----------------------------------------------------------------------------------------------
- SKT 0 0 0 | 0% 0%
- SKT 1 0 0 | 0% 0%
- ----------------------------------------------------------------------------------------------
- Total QPI incoming data traffic: 0 QPI data traffic/Memory controller traffic: 0.00
-
- Intel(r) QPI traffic estimation in bytes (data and non-data traffic outgoing from CPU/socket through QPI links):
-
- QPI0 QPI1 | QPI0 QPI1
- ----------------------------------------------------------------------------------------------
- SKT 0 0 0 | 0% 0%
- SKT 1 0 0 | 0% 0%
- ----------------------------------------------------------------------------------------------
- Total QPI outgoing data and non-data traffic: 0
-
- ----------------------------------------------------------------------------------------------
- SKT 0 package consumed 4.06 Joules
- SKT 1 package consumed 9.40 Joules
- ----------------------------------------------------------------------------------------------
- TOTAL: 13.46 Joules
-
- ----------------------------------------------------------------------------------------------
- SKT 0 DIMMs consumed 4.18 Joules
- SKT 1 DIMMs consumed 4.28 Joules
- ----------------------------------------------------------------------------------------------
- TOTAL: 8.47 Joules
- Cleaning up
-
-Â
-
-### pcm-sensor
-
-Can be used as a sensor for ksysguard GUI, which is currently not
-installed on Anselm.Â
-
-API
----
-
-In a similar fashion to PAPI, PCM provides a C++ API to access the
-performance counter from within your application. Refer to the [doxygen
-documentation](http://intel-pcm-api-documentation.github.io/classPCM.html)
-for details of the API.
-
-Due to security limitations, using PCM API to monitor your applications
-is currently not possible on Anselm. (The application must be run as
-root user)
-
-Sample program using the API :
-
- #include <stdlib.h>
- #include <stdio.h>
- #include "cpucounters.h"
-
- #define SIZE 1000
-
- using namespace std;
-
- int main(int argc, char **argv) {
- float matrixa[SIZE][SIZE], matrixb[SIZE][SIZE], mresult[SIZE][SIZE];
- float real_time, proc_time, mflops;
- long long flpins;
- int retval;
- int i,j,k;
-
- PCM * m = PCM::getInstance();
-
- if (m->program() != PCM::Success) return 1;
-
- SystemCounterState before_sstate = getSystemCounterState();
-
- /* Initialize the Matrix arrays */
- for ( i=0; i<SIZE*SIZE; i++ ){
- mresult[0][i] = 0.0;
- matrixa[0][i] = matrixb[0][i] = rand()*(float)1.1; }
-
- /* A naive Matrix-Matrix multiplication */
- for (i=0;i<SIZE;i++)
- for(j=0;j<SIZE;j++)
- for(k=0;k<SIZE;k++)
- mresult[i][j]=mresult[i][j] + matrixa[i][k]*matrixb[k][j];
-
- SystemCounterState after_sstate = getSystemCounterState();
-
- cout << "Instructions per clock:" << getIPC(before_sstate,after_sstate)
- << "L3 cache hit ratio:" << getL3CacheHitRatio(before_sstate,after_sstate)
- << "Bytes read:" << getBytesReadFromMC(before_sstate,after_sstate);
-
- for (i=0; i<SIZE;i++)
- for (j=0; j<SIZE; j++)
- if (mresult[i][j] == -1) printf("x");
-
- return 0;
- }
-
-Compile it with :
-
- $ icc matrix.cpp -o matrix -lpthread -lpcm
-
-Sample output :Â
-
- $ ./matrix
- Number of physical cores: 16
- Number of logical cores: 16
- Threads (logical cores) per physical core: 1
- Num sockets: 2
- Core PMU (perfmon) version: 3
- Number of core PMU generic (programmable) counters: 8
- Width of generic (programmable) counters: 48 bits
- Number of core PMU fixed counters: 3
- Width of fixed counters: 48 bits
- Nominal core frequency: 2400000000 Hz
- Package thermal spec power: 115 Watt; Package minimum power: 51 Watt; Package maximum power: 180 Watt;
- Socket 0: 1 memory controllers detected with total number of 4 channels. 2 QPI ports detected.
- Socket 1: 1 memory controllers detected with total number of 4 channels. 2 QPI ports detected.
- Number of PCM instances: 2
- Max QPI link speed: 16.0 GBytes/second (8.0 GT/second)
- Instructions per clock:1.7
- L3 cache hit ratio:1.0
- Bytes read:12513408
-
-References
-----------
-
-1. <https://software.intel.com/en-us/articles/intel-performance-counter-monitor-a-better-way-to-measure-cpu-utilization>
-2. <https://software.intel.com/sites/default/files/m/3/2/2/xeon-e5-2600-uncore-guide.pdf> Intel®
- Xeon® Processor E5-2600 Product Family Uncore Performance
- Monitoring Guide.
-3. <http://intel-pcm-api-documentation.github.io/classPCM.html>Â API
- Documentation
-
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-Intel VTune Amplifier
-=====================
-
-
-
-Introduction
-------------
-
-Intel*® *VTune™ >Amplifier, part of Intel Parallel studio, is a GUI
-profiling tool designed for Intel processors. It offers a graphical
-performance analysis of single core and multithreaded applications. A
-highlight of the features:
-
-- Hotspot analysis
-- Locks and waits analysis
-- Low level specific counters, such as branch analysis and memory
- bandwidth
-- Power usage analysis - frequency and sleep states.
-
-
-
-Usage
------
-
-To launch the GUI, first load the module:
-
- $ module add VTune/2016_update1
-
- class="s1">and launch the GUI :
-
- $Â amplxe-gui
-
-To profile an application with VTune Amplifier, special kernel
-modules need to be loaded. The modules are not loaded on Anselm login
-nodes, thus direct profiling on login nodes is not possible. Use VTune
-on compute nodes and refer to the documentation on [using GUI
-applications](https://docs.it4i.cz/anselm-cluster-documentation/software/debuggers/resolveuid/11e53ad0d2fd4c5187537f4baeedff33).
-
-The GUI will open in new window. Click on "*New Project...*" to
-create a new project. After clicking *OK*, a new window with project
-properties will appear. Â At "*Application:*", select the bath to your
-binary you want to profile (the binary should be compiled with -g flag).
-Some additional options such as command line arguments can be selected.
-At "*Managed code profiling mode:*" select "*Native*" (unless you want
-to profile managed mode .NET/Mono applications). After clicking *OK*,
-your project is created.
-
-To run a new analysis, click "*New analysis...*". You will see a list of
-possible analysis. Some of them will not be possible on the current CPU
-(eg. Intel Atom analysis is not possible on Sandy Bridge CPU), the GUI
-will show an error box if you select the wrong analysis. For example,
-select "*Advanced Hotspots*". Clicking on *Start *will start profiling
-of the application.
-
-Remote Analysis
----------------
-
-VTune Amplifier also allows a form of remote analysis. In this mode,
-data for analysis is collected from the command line without GUI, and
-the results are then loaded to GUI on another machine. This allows
-profiling without interactive graphical jobs. To perform a remote
-analysis, launch a GUI somewhere, open the new analysis window and then
-click the button "*Command line*" in bottom right corner. It will show
-the command line needed to perform the selected analysis.
-
-The command line will look like this:
-
- /apps/all/VTune/2016_update1/vtune_amplifier_xe_2016.1.1.434111/bin64/amplxe-cl -collect advanced-hotspots -knob collection-detail=stack-and-callcount -mrte-mode=native -target-duration-type=veryshort -app-working-dir /home/sta545/test -- /home/sta545/test_pgsesv
-
-Copy the line to clipboard and then you can paste it in your jobscript
-or in command line. After the collection is run, open the GUI once
-again, click the menu button in the upper right corner, and select
-"*Open > Result...*". The GUI will load the results from the run.
-
-Xeon Phi
---------
-
-This section is outdated. It will be updated with new information soon.
-
-It is possible to analyze both native and offload Xeon Phi applications.
-For offload mode, just specify the path to the binary. For native mode,
-you need to specify in project properties:
-
-Application: ssh
-
-Application parameters: mic0 source ~/.profile
-&& /path/to/your/bin
-
-Note that we include source ~/.profile
-in the command to setup environment paths [as described
-here](../intel-xeon-phi.html).Â
-
-If the analysis is interrupted or aborted, further analysis on the card
-might be impossible and you will get errors like "ERROR connecting to
-MIC card". In this case please contact our support to reboot the MIC
-card.
-
-You may also use remote analysis to collect data from the MIC and then
-analyze it in the GUI later :
-
- $ amplxe-cl -collect knc-hotspots -no-auto-finalize -- ssh mic0
- "export LD_LIBRARY_PATH=/apps/intel/composer_xe_2015.2.164/compiler/lib/mic/:/apps/intel/composer_xe_2015.2.164/mkl/lib/mic/; export KMP_AFFINITY=compact; /tmp/app.mic"
-
-References
-----------
-
-1. ><https://www.rcac.purdue.edu/tutorials/phi/PerformanceTuningXeonPhi-Tullos.pdf>Â Performance
- Tuning for Intel® Xeon Phi™ Coprocessors
-
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-PAPI
-====
-
-
-
-Introduction
-------------
-
- dir="auto">Performance Application Programming
-Interface >(PAPI)  is a portable interface to access
-hardware performance counters (such as instruction counts and cache
-misses) found in most modern architectures. With the new component
-framework, PAPI is not limited only to CPU counters, but offers also
-components for CUDA, network, Infiniband etc.
-
-PAPI provides two levels of interface - a simpler, high level
-interface and more detailed low level interface.
-
-PAPI can be used with parallel as well as serial programs.
-
-Usage
------
-
-To use PAPI, load
-[module](../../environment-and-modules.html)
-papi :
-
- $ module load papi
-
-This will load the default version. Execute
-module avail papi for a list of installed
-versions.
-
-Utilites
---------
-
-The bin directory of PAPI (which is
-automatically added to $PATH upon
-loading the module) contains various utilites.
-
-### papi_avail
-
-Prints which preset events are available on the current CPU. The third
-column indicated whether the preset event is available on the current
-CPU.
-
- $ papi_avail
- Available events and hardware information.
- --------------------------------------------------------------------------------
- PAPI Version : 5.3.2.0
- Vendor string and code : GenuineIntel (1)
- Model string and code : Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz (45)
- CPU Revision : 7.000000
- CPUID Info : Family: 6 Model: 45 Stepping: 7
- CPU Max Megahertz : 2601
- CPU Min Megahertz : 1200
- Hdw Threads per core : 1
- Cores per Socket : 8
- Sockets : 2
- NUMA Nodes : 2
- CPUs per Node : 8
- Total CPUs : 16
- Running in a VM : no
- Number Hardware Counters : 11
- Max Multiplex Counters : 32
- --------------------------------------------------------------------------------
- Name Code Avail Deriv Description (Note)
- PAPI_L1_DCM 0x80000000 Yes No Level 1 data cache misses
- PAPI_L1_ICM 0x80000001 Yes No Level 1 instruction cache misses
- PAPI_L2_DCM 0x80000002 Yes Yes Level 2 data cache misses
- PAPI_L2_ICM 0x80000003 Yes No Level 2 instruction cache misses
- PAPI_L3_DCM 0x80000004 No No Level 3 data cache misses
- PAPI_L3_ICM 0x80000005 No No Level 3 instruction cache misses
- PAPI_L1_TCM 0x80000006 Yes Yes Level 1 cache misses
- PAPI_L2_TCM 0x80000007 Yes No Level 2 cache misses
- PAPI_L3_TCM 0x80000008 Yes No Level 3 cache misses
- ....Â
-
-### papi_native_avail
-
-Prints which native events are available on the current
-CPU.
-
-### class="s1">papi_cost
-
-Measures the cost (in cycles) of basic PAPI operations.
-
-###papi_mem_info
-
-Prints information about the memory architecture of the current
-CPU.
-
-PAPI API
---------
-
-PAPI provides two kinds of events:Â
-
-- **Preset events** is a set of predefined common CPU events,
- >standardized across platforms.
-- **Native events **is a set of all events supported by the
- current hardware. This is a larger set of features than preset. For
- other components than CPU, only native events are usually available.
-
-To use PAPI in your application, you need to link the appropriate
-include file.
-
-- papi.h for C
-- f77papi.h for Fortran 77
-- f90papi.h for Fortran 90
-- fpapi.h for Fortran with preprocessor
-
-The include path is automatically added by papi module to
-$INCLUDE.
-
-### High level API
-
-Please refer
-to <http://icl.cs.utk.edu/projects/papi/wiki/PAPIC:High_Level> for a
-description of the High level API.
-
-### Low level API
-
-Please refer
-to <http://icl.cs.utk.edu/projects/papi/wiki/PAPIC:Low_Level> for a
-description of the Low level API.
-
-### Timers
-
-PAPI provides the most accurate timers the platform can support.
-See <http://icl.cs.utk.edu/projects/papi/wiki/PAPIC:Timers>
-
-### System information
-
-PAPI can be used to query some system infromation, such as CPU name and
-MHz.
-See <http://icl.cs.utk.edu/projects/papi/wiki/PAPIC:System_Information>
-
-Example
--------
-
-The following example prints MFLOPS rate of a naive matrix-matrix
-multiplication :
-
- #include <stdlib.h>
- #include <stdio.h>
- #include "papi.h"
- #define SIZE 1000
-
- int main(int argc, char **argv) {
- float matrixa[SIZE][SIZE], matrixb[SIZE][SIZE], mresult[SIZE][SIZE];
- float real_time, proc_time, mflops;
- long long flpins;
- int retval;
- int i,j,k;
-
- /* Initialize the Matrix arrays */
- for ( i=0; i<SIZE*SIZE; i++ ){
- mresult[0][i] = 0.0;
- matrixa[0][i] = matrixb[0][i] = rand()*(float)1.1;Â
- }
- Â
- /* Setup PAPI library and begin collecting data from the counters */
- if((retval=PAPI_flops( &real_time, &proc_time, &flpins, &mflops))<PAPI_OK)
- printf("Error!");
-
- /* A naive Matrix-Matrix multiplication */
- for (i=0;i<SIZE;i++)
- for(j=0;j<SIZE;j++)
- for(k=0;k<SIZE;k++)
- mresult[i][j]=mresult[i][j] + matrixa[i][k]*matrixb[k][j];
-
- /* Collect the data into the variables passed in */
- if((retval=PAPI_flops( &real_time, &proc_time, &flpins, &mflops))<PAPI_OK)
- printf("Error!");
-
- printf("Real_time:t%fnProc_time:t%fnTotal flpins:t%lldnMFLOPS:tt%fn", real_time, proc_time, flpins, mflops);
- PAPI_shutdown();
- return 0;
- }
-
-Â Now compile and run the example :
-
- $ gcc matrix.c -o matrix -lpapi
- $ ./matrix
- Real_time: 8.852785
- Proc_time: 8.850000
- Total flpins: 6012390908
- MFLOPS: 679.366211Â
-
-Let's try with optimizations enabled :
-
- $ gcc -O3 matrix.c -o matrix -lpapi
- $ ./matrix
- Real_time: 0.000020
- Proc_time: 0.000000
- Total flpins: 6
- MFLOPS: inf
-
-Now we see a seemingly strange result - the multiplication took no time
-and only 6 floating point instructions were issued. This is because the
-compiler optimizations have completely removed the multiplication loop,
-as the result is actually not used anywhere in the program. We can fix
-this by adding some "dummy" code at the end of the Matrix-Matrix
-multiplication routine :
-
- for (i=0; i<SIZE;i++)
- for (j=0; j<SIZE; j++)
- if (mresult[i][j] == -1.0) printf("x");
-
-Now the compiler won't remove the multiplication loop. (However it is
-still not that smart to see that the result won't ever be negative). Now
-run the code again:
-
- $ gcc -O3 matrix.c -o matrix -lpapi
- $ ./matrix
- Real_time: 8.795956
- Proc_time: 8.790000
- Total flpins: 18700983160
- MFLOPS: 2127.529297Â
-
-### Intel Xeon Phi
-
-PAPI currently supports only a subset of counters on the Intel Xeon Phi
-processor compared to Intel Xeon, for example the floating point
-operations counter is missing.
-
-To use PAPI in [Intel Xeon
-Phi](../intel-xeon-phi.html)Â native applications, you
-need to load module with " -mic" suffix,
-for example " papi/5.3.2-mic" :
-
- $ module load papi/5.3.2-mic
-
-Then, compile your application in the following way:
-
- $ module load intel
- $ icc -mmic -Wl,-rpath,/apps/intel/composer_xe_2013.5.192/compiler/lib/mic matrix-mic.c -o matrix-mic -lpapi -lpfm
-
-To execute the application on MIC, you need to manually setÂ
-LD_LIBRARY_PATH :
-
- $Â qsub -q qmic -A NONE-0-0 -I
- $ ssh mic0
- $ export LD_LIBRARY_PATH=/apps/tools/papi/5.4.0-mic/lib/Â
- $ ./matrix-micÂ
-
-Alternatively, you can link PAPI statically (
--static flag), then
-LD_LIBRARY_PATH does not need to be set.
-
-You can also execute the PAPI tools on MIC :
-
- $ /apps/tools/papi/5.4.0-mic/bin/papi_native_avail
-
-To use PAPI in offload mode, you need to provide both host and MIC
-versions of PAPI:
-
- $ module load papi/5.4.0
- $ icc matrix-offload.c -o matrix-offload -offload-option,mic,compiler,"-L$PAPI_HOME-mic/lib -lpapi" -lpapi
-
-References
-----------
-
-1. <http://icl.cs.utk.edu/papi/>Â Main project page
-2. <http://icl.cs.utk.edu/projects/papi/wiki/Main_Page>Â Wiki
-3. <http://icl.cs.utk.edu/papi/docs/> API Documentation
-
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-Scalasca
-========
-
-Introduction
--------------------------
-
-[Scalasca](http://www.scalasca.org/) is a software tool
-that supports the performance optimization of parallel programs by
-measuring and analyzing their runtime behavior. The analysis identifies
-potential performance bottlenecks – in particular those concerning
-communication and synchronization – and offers guidance in exploring
-their causes.
-
-Scalasca supports profiling of MPI, OpenMP and hybrid MPI+OpenMP
-applications.
-
-Installed versions
-------------------
-
-There are currently two versions of Scalasca 2.0
-[modules](../../environment-and-modules.html) installed
-on Anselm:
-
-- class="s1">
- scalasca2/2.0-gcc-openmpi, for usage with
- [GNU Compiler](../compilers.html) and
- [OpenMPI](../mpi-1/Running_OpenMPI.html),
-
-- class="s1">
- scalasca2/2.0-icc-impi, for usage with
- [Intel Compiler](../compilers.html) and [Intel
- MPI](../mpi-1/running-mpich2.html).
-
-Usage
------
-
-Profiling a parallel application with Scalasca consists of three steps:
-
-1. Instrumentation, compiling the application such way, that the
- profiling data can be generated.
-2. Runtime measurement, running the application with the Scalasca
- profiler to collect performance data.
-3. Analysis of reports
-
-### Instrumentation
-
-Instrumentation via " scalasca
--instrument" is discouraged. Use [Score-P
-instrumentation](score-p.html).
-
-### Runtime measurement
-
-After the application is instrumented, runtime measurement can be
-performed with the " scalasca -analyze"
-command. The syntax is :
-
- scalasca -analyze [scalasca options]
-[launcher] [launcher options] [program] [program options]
-
-An example :
-
- $ scalasca -analyze mpirun -np 4 ./mympiprogram
-
-Some notable Scalsca options are:
-
--t Enable trace data collection. By default, only summary data are
-collected.
--e <directory> Specify a directory to save the collected data to.
-By default, Scalasca saves the data to a directory with
-prefix >scorep_, followed by name of the executable and launch
-configuration.
-
-Scalasca can generate a huge amount of data, especially if tracing is
-enabled. Please consider saving the data to a [scratch
-directory](../../storage.html).
-
-### Analysis of reports
-
-For the analysis, you must have [Score-P](score-p.html)
-and [CUBE](cube.html) modules loaded. The analysis is
-done in two steps, first, the data is preprocessed and then CUBE GUI
-tool is launched.
-
-To launch the analysis, run :
-
-`
-scalasca -examine [options] <experiment_directory>
-`
-
-If you do not wish to launch the GUI tool, use the "-s" option :
-
-`
-scalasca -examine -s <experiment_directory>
-`
-
-Alternatively you can open CUBE and load the data directly from here.
-Keep in mind that in that case the preprocessing is not done and not all
-metrics will be shown in the viewer.
-
-Refer to [CUBE documentation](cube.html)Â on usage of the
-GUI viewer.
-
-References
-----------
-
-1. <http://www.scalasca.org/>
-
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-Score-P
-=======
-
-Introduction
-------------
-
-The [Score-P measurement
-infrastructure](http://www.vi-hps.org/projects/score-p/)
-is a highly scalable and easy-to-use tool suite for profiling, event
-tracing, and online analysis of HPC applications.Â
-
-Score-P can be used as an instrumentation tool for
-[Scalasca](scalasca.html).
-
-Installed versions
-------------------
-
-There are currently two versions of Score-P version 1.2.6
-[modules](../../environment-and-modules.html)Â installed
-on Anselm :
-
-- class="s1">scorep/1.2.3-gcc-openmpi>, for usage
- with [GNU
- Compiler](../compilers.html)> and [OpenMPI](../mpi-1/Running_OpenMPI.html){.internal>,
-
-- class="s1">scorep/1.2.3-icc-impi>, for usage
- with [Intel
- Compiler](../compilers.html)> and [Intel
- MPI](../mpi-1/running-mpich2.html)>.
-
-Instrumentation
----------------
-
-There are three ways to instrument your parallel applications in
-order to enable performance data collection :
-
-1. >Automated instrumentation using compiler
-2. >Manual instrumentation using API calls
-3. >Manual instrumentation using directives
-
-### Automated instrumentation
-
-is the easiest method. Score-P will automatically add instrumentation to
-every routine entry and exit using compiler hooks, and will intercept
-MPI calls and OpenMP regions. This method might, however, produce a
-large number of data. If you want to focus on profiler a specific
-regions of your code, consider using the manual instrumentation methods.
-To use automated instrumentation, simply prepend
-scorep to your compilation command. For
-example, replace :Â
-
-`
-$ mpif90 -c foo.f90
-$ mpif90 -c bar.f90
-$ mpif90 -o myapp foo.o bar.o
-`
-
-with :
-
-`
-$ scorep mpif90 -c foo.f90
-$ scorep mpif90 -c bar.f90
-$ scorep mpif90 -o myapp foo.o bar.o
-`
-
-Usually your program is compiled using a Makefile or similar script, so
-it advisable to add the scorep command to
-your definition of variables CC,
-CXX, class="monospace">FCC etc.
-
-It is important that scorep is prepended
-also to the linking command, in order to link with Score-P
-instrumentation libraries.
-
-###Manual instrumentation using API calls
-
-To use this kind of instrumentation, use
-scorep with switch
---user. You will then mark regions to be
-instrumented by inserting API calls.
-
-An example in C/C++ :
-
- #include <scorep/SCOREP_User.h>
- void foo()
- {
- SCOREP_USER_REGION_DEFINE( my_region_handle )
- // more declarations
- SCOREP_USER_REGION_BEGIN( my_region_handle, "foo", SCOREP_USER_REGION_TYPE_COMMON )
- // do something
- SCOREP_USER_REGION_END( my_region_handle )
- }
-
-Â and Fortran :
-
- #include "scorep/SCOREP_User.inc"
- subroutine foo
- SCOREP_USER_REGION_DEFINE( my_region_handle )
- ! more declarations
- SCOREP_USER_REGION_BEGIN( my_region_handle, "foo", SCOREP_USER_REGION_TYPE_COMMON )
- ! do something
- SCOREP_USER_REGION_END( my_region_handle )
- end subroutine foo
-
-Please refer to the [documentation for description of the
-API](https://silc.zih.tu-dresden.de/scorep-current/pdf/scorep.pdf).
-
-###Manual instrumentation using directives
-
-This method uses POMP2 directives to mark regions to be instrumented. To
-use this method, use command scorep
---pomp.
-
-Example directives in C/C++ :
-
- void foo(...)
- {
- /* declarations */
- #pragma pomp inst begin(foo)
- ...
- if (<condition>)
- {
- #pragma pomp inst altend(foo)
- return;
- }
- ...
- #pragma pomp inst end(foo)
- }
-
-and in Fortran :
-
- subroutine foo(...)
- !declarations
- !POMP$ INST BEGIN(foo)
- ...
- if (<condition>) then
- !POMP$ INST ALTEND(foo)
- return
- end if
- ...
- !POMP$ INST END(foo)
- end subroutine foo
-
-The directives are ignored if the program is compiled without Score-P.
-Again, please refer to the
-[documentation](https://silc.zih.tu-dresden.de/scorep-current/pdf/scorep.pdf)
-for a more elaborate description.
-
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-Debuggers and profilers summary
-===============================
-
-
-
-Introduction
-------------
-
-We provide state of the art programms and tools to develop, profile and
-debug HPC codes at IT4Innovations.
-On these pages, we provide an overview of the profiling and debugging
-tools available on Anslem at IT4I.
-
-Intel debugger
---------------
-
-The intel debugger version 13.0 is available, via module intel. The
-debugger works for applications compiled with C and C++ compiler and the
-ifort fortran 77/90/95 compiler. The debugger provides java GUI
-environment. Use [X
-display](https://docs.it4i.cz/anselm-cluster-documentation/software/debuggers/resolveuid/11e53ad0d2fd4c5187537f4baeedff33)
-for running the GUI.
-
- $ module load intel
- $ idb
-
-Read more at the [Intel
-Debugger](../intel-suite/intel-debugger.html) page.
-
-Allinea Forge (DDT/MAP)
------------------------
-
-Allinea DDT, is a commercial debugger primarily for debugging parallel
-MPI or OpenMP programs. It also has a support for GPU (CUDA) and Intel
-Xeon Phi accelerators. DDT provides all the standard debugging features
-(stack trace, breakpoints, watches, view variables, threads etc.) for
-every thread running as part of your program, or for every process -
-even if these processes are distributed across a cluster using an MPI
-implementation.
-
- $ module load Forge
- $ forge
-
-Read more at the [Allinea DDT](allinea-ddt.html) page.
-
-Allinea Performance Reports
----------------------------
-
-Allinea Performance Reports characterize the performance of HPC
-application runs. After executing your application through the tool, a
-synthetic HTML report is generated automatically, containing information
-about several metrics along with clear behavior statements and hints to
-help you improve the efficiency of your runs. Our license is limited to
-64 MPI processes.
-
- $ module load PerformanceReports/6.0
- $ perf-report mpirun -n 64 ./my_application argument01 argument02
-
-Read more at the [Allinea Performance
-Reports](allinea-performance-reports.html) page.
-
-RougeWave Totalview
--------------------
-
-TotalView is a source- and machine-level debugger for multi-process,
-multi-threaded programs. Its wide range of tools provides ways to
-analyze, organize, and test programs, making it easy to isolate and
-identify problems in individual threads and processes in programs of
-great complexity.
-
- $ module load totalview
- $ totalview
-
-Read more at the [Totalview](total-view.html) page.
-
-Vampir trace analyzer
----------------------
-
-Vampir is a GUI trace analyzer for traces in OTF format.
-
- $ module load Vampir/8.5.0
- $ vampir
-
-Read more at
-the [Vampir](../../../salomon/software/debuggers/vampir.html) page.
-
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-Total View
-==========
-
-TotalView is a GUI-based source code multi-process, multi-thread
-debugger.
-
-License and Limitations for Anselm Users
-----------------------------------------
-
-On Anselm users can debug OpenMP or MPI code that runs up to 64 parallel
-processes. These limitation means that:
-
-Â Â Â 1 user can debug up 64 processes, or
-Â Â Â 32 users can debug 2 processes, etc.
-
-Debugging of GPU accelerated codes is also supported.
-
-You can check the status of the licenses here:
-
- cat /apps/user/licenses/totalview_features_state.txt
-
- # totalview
- # -------------------------------------------------
- # FEATUREÂ Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â TOTALÂ Â USEDÂ AVAIL
- # -------------------------------------------------
- TotalView_Team                    64     0    64
- Replay                            64     0    64
- CUDAÂ Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â 64Â Â Â Â Â 0Â Â Â Â 64
-
-Compiling Code to run with TotalView
-------------------------------------
-
-### Modules
-
-Load all necessary modules to compile the code. For example:
-
- module load intel
-
- module load impi  ... or ... module load openmpi/X.X.X-icc
-
-Load the TotalView module:
-
- module load totalview/8.12
-
-Compile the code:
-
- mpicc -g -O0 -o test_debug test.c
-
- mpif90 -g -O0 -o test_debug test.f
-
-### Compiler flags
-
-Before debugging, you need to compile your code with theses flags:
-
--g** : Generates extra debugging information usable by GDB. -g3**
-includes even more debugging information. This option is available for
-GNU and INTEL C/C++ and Fortran compilers.
-
--O0** : Suppress all optimizations.**
-
-Starting a Job with TotalView
------------------------------
-
-Be sure to log in with an X window forwarding enabled. This could mean
-using the -X in the ssh:Â
-
- ssh -X username@anselm.it4i.cz
-
-Other options is to access login node using VNC. Please see the detailed
-information on how to use graphic user interface on Anselm
-[here](https://docs.it4i.cz/anselm-cluster-documentation/software/debuggers/resolveuid/11e53ad0d2fd4c5187537f4baeedff33#VNC).
-
-From the login node an interactive session with X windows forwarding (-X
-option) can be started by following command:
-
- qsub -I -X -A NONE-0-0 -q qexp -lselect=1:ncpus=16:mpiprocs=16,walltime=01:00:00
-
-Then launch the debugger with the totalview command followed by the name
-of the executable to debug.
-
-### Debugging a serial code
-
-To debug a serial code use:
-
- totalview test_debug
-
-### Debugging a parallel code - option 1
-
-To debug a parallel code compiled with >**OpenMPI** you need
-to setup your TotalView environment:Â
-
-Please note:** To be able to run parallel debugging procedure from the
-command line without stopping the debugger in the mpiexec source code
-you have to add the following function to your **~/.tvdrc** file:
-
- proc mpi_auto_run_starter {loaded_id} {
- Â Â Â set starter_programs {mpirun mpiexec orterun}
- Â Â Â set executable_name [TV::symbol get $loaded_id full_pathname]
- Â Â Â set file_component [file tail $executable_name]
-
- Â Â Â if {[lsearch -exact $starter_programs $file_component] != -1} {
- Â Â Â Â Â Â Â puts "*************************************"
- Â Â Â Â Â Â Â puts "Automatically starting $file_component"
- Â Â Â Â Â Â Â puts "*************************************"
- Â Â Â Â Â Â Â dgo
- Â Â Â }
- }
-
- # Append this function to TotalView's image load callbacks so that
- # TotalView run this program automatically.
-
- dlappend TV::image_load_callbacks mpi_auto_run_starter
-
-The source code of this function can be also found in
-
- /apps/mpi/openmpi/intel/1.6.5/etc/openmpi-totalview.tcl
-
-You can also add only following line to you ~/.tvdrc file instead of
-the entire function:
-
-source /apps/mpi/openmpi/intel/1.6.5/etc/openmpi-totalview.tcl**
-
-You need to do this step only once.
-
-Now you can run the parallel debugger using:
-
- mpirun -tv -n 5 ./test_debug
-
-When following dialog appears click on "Yes"
-
-
-
-At this point the main TotalView GUI window will appear and you can
-insert the breakpoints and start debugging:
-
-
-
-### Debugging a parallel code - option 2
-
-Other option to start new parallel debugging session from a command line
-is to let TotalView to execute mpirun by itself. In this case user has
-to specify a MPI implementation used to compile the source code.Â
-
-The following example shows how to start debugging session with Intel
-MPI:
-
- module load intel/13.5.192 impi/4.1.1.036 totalview/8/13
-
- totalview -mpi "Intel MPI-Hydra" -np 8 ./hello_debug_impi
-
-After running previous command you will see the same window as shown in
-the screenshot above.
-
-More information regarding the command line parameters of the TotalView
-can be found TotalView Reference Guide, Chapter 7: TotalView Command
-Syntax. Â
-
-Documentation
--------------
-
-[1] The [TotalView
-documentation](http://www.roguewave.com/support/product-documentation/totalview-family.aspx#totalview)
-web page is a good resource for learning more about some of the advanced
-TotalView features.
-
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--- a/converted/docs.it4i.cz/anselm-cluster-documentation/software/debuggers/valgrind.md
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-Valgrind
-========
-
-Valgrind is a tool for memory debugging and profiling.
-
-About Valgrind
---------------
-
-Valgrind is an open-source tool, used mainly for debuggig memory-related
-problems, such as memory leaks, use of uninitalized memory etc. in C/C++
-applications. The toolchain was however extended over time with more
-functionality, such as debugging of threaded applications, cache
-profiling, not limited only to C/C++.
-
-Valgind is an extremely useful tool for debugging memory errors such as
-[off-by-one](http://en.wikipedia.org/wiki/Off-by-one_error).
-Valgrind uses a virtual machine and dynamic recompilation of binary
-code, because of that, you can expect that programs being debugged by
-Valgrind run 5-100 times slower.
-
-The main tools available in Valgrind are :
-
-- **Memcheck**, the original, must used and default tool. Verifies
- memory access in you program and can detect use of unitialized
- memory, out of bounds memory access, memory leaks, double free, etc.
-- **Massif**, a heap profiler.
-- **Hellgrind** and **DRD** can detect race conditions in
- multi-threaded applications.
-- **Cachegrind**, a cache profiler.
-- **Callgrind**, a callgraph analyzer.
-- For a full list and detailed documentation, please refer to the
- [official Valgrind
- documentation](http://valgrind.org/docs/).
-
-Installed versions
-------------------
-
-There are two versions of Valgrind available on Anselm.
-
-- >Version 3.6.0, installed by operating system vendor
- in /usr/bin/valgrind.
- >This version is available by default, without the need
- to load any module. This version however does not provide additional
- MPI support.
-- >Version 3.9.0 with support for Intel MPI, available in
- [module](../../environment-and-modules.html)Â
- valgrind/3.9.0-impi. After loading the
- module, this version replaces the default valgrind.
-
-Usage
------
-
-Compile the application which you want to debug as usual. It is
-advisable to add compilation flags -g (to
-add debugging information to the binary so that you will see original
-source code lines in the output) and -O0
-(to disable compiler optimizations).Â
-
-For example, lets look at this C code, which has two problems :
-
- #include <stdlib.h>
-
- void f(void)
- {
- int* x = malloc(10 * sizeof(int));
- x[10] = 0; // problem 1: heap block overrun
- } // problem 2: memory leak -- x not freed
-
- int main(void)
- {
- f();
- return 0;
- }
-
-Now, compile it with Intel compiler :
-
- $ module add intel
- $ icc -g valgrind-example.c -o valgrind-exampleÂ
-
-Now, lets run it with Valgrind. The syntax is :
-
- valgrind [valgrind options] <your program
-binary> [your program options]
-
-If no Valgrind options are specified, Valgrind defaults to running
-Memcheck tool. Please refer to the Valgrind documentation for a full
-description of command line options.
-
- $ valgrind ./valgrind-example
- ==12652== Memcheck, a memory error detector
- ==12652== Copyright (C) 2002-2013, and GNU GPL'd, by Julian Seward et al.
- ==12652== Using Valgrind-3.9.0 and LibVEX; rerun with -h for copyright info
- ==12652== Command: ./valgrind-example
- ==12652==
- ==12652== Invalid write of size 4
- ==12652== at 0x40053E: f (valgrind-example.c:6)
- ==12652== by 0x40054E: main (valgrind-example.c:11)
- ==12652== Address 0x5861068 is 0 bytes after a block of size 40 alloc'd
- ==12652== at 0x4C27AAA: malloc (vg_replace_malloc.c:291)
- ==12652== by 0x400528: f (valgrind-example.c:5)
- ==12652== by 0x40054E: main (valgrind-example.c:11)
- ==12652==
- ==12652==
- ==12652== HEAP SUMMARY:
- ==12652== in use at exit: 40 bytes in 1 blocks
- ==12652== total heap usage: 1 allocs, 0 frees, 40 bytes allocated
- ==12652==
- ==12652== LEAK SUMMARY:
- ==12652== definitely lost: 40 bytes in 1 blocks
- ==12652== indirectly lost: 0 bytes in 0 blocks
- ==12652== possibly lost: 0 bytes in 0 blocks
- ==12652== still reachable: 0 bytes in 0 blocks
- ==12652== suppressed: 0 bytes in 0 blocks
- ==12652== Rerun with --leak-check=full to see details of leaked memory
- ==12652==
- ==12652== For counts of detected and suppressed errors, rerun with: -v
- ==12652== ERROR SUMMARY: 1 errors from 1 contexts (suppressed: 6 from 6)
-
-In the output we can see that Valgrind has detected both errors - the
-off-by-one memory access at line 5 and a memory leak of 40 bytes. If we
-want a detailed analysis of the memory leak, we need to run Valgrind
-with --leak-check=full option :
-
- $ valgrind --leak-check=full ./valgrind-example
- ==23856== Memcheck, a memory error detector
- ==23856== Copyright (C) 2002-2010, and GNU GPL'd, by Julian Seward et al.
- ==23856== Using Valgrind-3.6.0 and LibVEX; rerun with -h for copyright info
- ==23856== Command: ./valgrind-example
- ==23856==
- ==23856== Invalid write of size 4
- ==23856== at 0x40067E: f (valgrind-example.c:6)
- ==23856== by 0x40068E: main (valgrind-example.c:11)
- ==23856== Address 0x66e7068 is 0 bytes after a block of size 40 alloc'd
- ==23856== at 0x4C26FDE: malloc (vg_replace_malloc.c:236)
- ==23856== by 0x400668: f (valgrind-example.c:5)
- ==23856== by 0x40068E: main (valgrind-example.c:11)
- ==23856==
- ==23856==
- ==23856== HEAP SUMMARY:
- ==23856== in use at exit: 40 bytes in 1 blocks
- ==23856== total heap usage: 1 allocs, 0 frees, 40 bytes allocated
- ==23856==
- ==23856== 40 bytes in 1 blocks are definitely lost in loss record 1 of 1
- ==23856== at 0x4C26FDE: malloc (vg_replace_malloc.c:236)
- ==23856== by 0x400668: f (valgrind-example.c:5)
- ==23856== by 0x40068E: main (valgrind-example.c:11)
- ==23856==
- ==23856== LEAK SUMMARY:
- ==23856== definitely lost: 40 bytes in 1 blocks
- ==23856== indirectly lost: 0 bytes in 0 blocks
- ==23856== possibly lost: 0 bytes in 0 blocks
- ==23856== still reachable: 0 bytes in 0 blocks
- ==23856== suppressed: 0 bytes in 0 blocks
- ==23856==
- ==23856== For counts of detected and suppressed errors, rerun with: -v
- ==23856== ERROR SUMMARY: 2 errors from 2 contexts (suppressed: 6 from 6)
-
-Now we can see that the memory leak is due to the
-malloc() at line 6.
-
-Usage with MPI
----------------------------
-
-Although Valgrind is not primarily a parallel debugger, it can be used
-to debug parallel applications as well. When launching your parallel
-applications, prepend the valgrind command. For example :
-
- $ mpirun -np 4 valgrind myapplication
-
-The default version without MPI support will however report a large
-number of false errors in the MPI library, such as :
-
- ==30166== Conditional jump or move depends on uninitialised value(s)
- ==30166== at 0x4C287E8: strlen (mc_replace_strmem.c:282)
- ==30166== by 0x55443BD: I_MPI_Processor_model_number (init_interface.c:427)
- ==30166== by 0x55439E0: I_MPI_Processor_arch_code (init_interface.c:171)
- ==30166== by 0x558D5AE: MPID_nem_impi_init_shm_configuration (mpid_nem_impi_extensions.c:1091)
- ==30166== by 0x5598F4C: MPID_nem_init_ckpt (mpid_nem_init.c:566)
- ==30166== by 0x5598B65: MPID_nem_init (mpid_nem_init.c:489)
- ==30166== by 0x539BD75: MPIDI_CH3_Init (ch3_init.c:64)
- ==30166== by 0x5578743: MPID_Init (mpid_init.c:193)
- ==30166== by 0x554650A: MPIR_Init_thread (initthread.c:539)
- ==30166== by 0x553369F: PMPI_Init (init.c:195)
- ==30166== by 0x4008BD: main (valgrind-example-mpi.c:18)
-
-so it is better to use the MPI-enabled valgrind from module. The MPI
-version requires libraryÂ
-/apps/tools/valgrind/3.9.0/impi/lib/valgrind/libmpiwrap-amd64-linux.so,
-which must be included in the LD_PRELOAD
-environment variable.
-
-Lets look at this MPI example :
-
- #include <stdlib.h>
- #include <mpi.h>Â
-
- int main(int argc, char *argv[])
- {
- Â Â Â Â Â int *data = malloc(sizeof(int)*99);
-
- Â Â Â Â Â MPI_Init(&argc, &argv);
- Â Â Â Â MPI_Bcast(data, 100, MPI_INT, 0, MPI_COMM_WORLD);
- Â Â Â Â Â MPI_Finalize();Â
-
- Â Â Â Â Â Â Â return 0;
- }
-
-There are two errors - use of uninitialized memory and invalid length of
-the buffer. Lets debug it with valgrind :
-
- $ module add intel impi
- $ mpicc -g valgrind-example-mpi.c -o valgrind-example-mpi
- $ module add valgrind/3.9.0-impi
- $ mpirun -np 2 -env LD_PRELOAD /apps/tools/valgrind/3.9.0/impi/lib/valgrind/libmpiwrap-amd64-linux.so valgrind ./valgrind-example-mpi
-
-Prints this output : (note that there is output printed for every
-launched MPI process)
-
- ==31318== Memcheck, a memory error detector
- ==31318== Copyright (C) 2002-2013, and GNU GPL'd, by Julian Seward et al.
- ==31318== Using Valgrind-3.9.0 and LibVEX; rerun with -h for copyright info
- ==31318== Command: ./valgrind-example-mpi
- ==31318==
- ==31319== Memcheck, a memory error detector
- ==31319== Copyright (C) 2002-2013, and GNU GPL'd, by Julian Seward et al.
- ==31319== Using Valgrind-3.9.0 and LibVEX; rerun with -h for copyright info
- ==31319== Command: ./valgrind-example-mpi
- ==31319==
- valgrind MPI wrappers 31319: Active for pid 31319
- valgrind MPI wrappers 31319: Try MPIWRAP_DEBUG=help for possible options
- valgrind MPI wrappers 31318: Active for pid 31318
- valgrind MPI wrappers 31318: Try MPIWRAP_DEBUG=help for possible options
- ==31319== Unaddressable byte(s) found during client check request
- ==31319== at 0x4E35974: check_mem_is_addressable_untyped (libmpiwrap.c:960)
- ==31319== by 0x4E5D0FE: PMPI_Bcast (libmpiwrap.c:908)
- ==31319== by 0x400911: main (valgrind-example-mpi.c:20)
- ==31319== Address 0x69291cc is 0 bytes after a block of size 396 alloc'd
- ==31319== at 0x4C27AAA: malloc (vg_replace_malloc.c:291)
- ==31319== by 0x4007BC: main (valgrind-example-mpi.c:8)
- ==31319==
- ==31318== Uninitialised byte(s) found during client check request
- ==31318== at 0x4E3591D: check_mem_is_defined_untyped (libmpiwrap.c:952)
- ==31318== by 0x4E5D06D: PMPI_Bcast (libmpiwrap.c:908)
- ==31318== by 0x400911: main (valgrind-example-mpi.c:20)
- ==31318== Address 0x6929040 is 0 bytes inside a block of size 396 alloc'd
- ==31318== at 0x4C27AAA: malloc (vg_replace_malloc.c:291)
- ==31318== by 0x4007BC: main (valgrind-example-mpi.c:8)
- ==31318==
- ==31318== Unaddressable byte(s) found during client check request
- ==31318== at 0x4E3591D: check_mem_is_defined_untyped (libmpiwrap.c:952)
- ==31318== by 0x4E5D06D: PMPI_Bcast (libmpiwrap.c:908)
- ==31318== by 0x400911: main (valgrind-example-mpi.c:20)
- ==31318== Address 0x69291cc is 0 bytes after a block of size 396 alloc'd
- ==31318== at 0x4C27AAA: malloc (vg_replace_malloc.c:291)
- ==31318== by 0x4007BC: main (valgrind-example-mpi.c:8)
- ==31318==
- ==31318==
- ==31318== HEAP SUMMARY:
- ==31318== in use at exit: 3,172 bytes in 67 blocks
- ==31318== total heap usage: 191 allocs, 124 frees, 81,203 bytes allocated
- ==31318==
- ==31319==
- ==31319== HEAP SUMMARY:
- ==31319== in use at exit: 3,172 bytes in 67 blocks
- ==31319== total heap usage: 175 allocs, 108 frees, 48,435 bytes allocated
- ==31319==
- ==31318== LEAK SUMMARY:
- ==31318== definitely lost: 408 bytes in 3 blocks
- ==31318== indirectly lost: 256 bytes in 1 blocks
- ==31318== possibly lost: 0 bytes in 0 blocks
- ==31318== still reachable: 2,508 bytes in 63 blocks
- ==31318== suppressed: 0 bytes in 0 blocks
- ==31318== Rerun with --leak-check=full to see details of leaked memory
- ==31318==
- ==31318== For counts of detected and suppressed errors, rerun with: -v
- ==31318== Use --track-origins=yes to see where uninitialised values come from
- ==31318== ERROR SUMMARY: 2 errors from 2 contexts (suppressed: 4 from 4)
- ==31319== LEAK SUMMARY:
- ==31319== definitely lost: 408 bytes in 3 blocks
- ==31319== indirectly lost: 256 bytes in 1 blocks
- ==31319== possibly lost: 0 bytes in 0 blocks
- ==31319== still reachable: 2,508 bytes in 63 blocks
- ==31319== suppressed: 0 bytes in 0 blocks
- ==31319== Rerun with --leak-check=full to see details of leaked memory
- ==31319==
- ==31319== For counts of detected and suppressed errors, rerun with: -v
- ==31319== ERROR SUMMARY: 1 errors from 1 contexts (suppressed: 4 from 4)
-
-We can see that Valgrind has reported use of unitialised memory on the
-master process (which reads the array to be broadcasted) and use of
-unaddresable memory on both processes.
-
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@@ -1,33 +0,0 @@
-Vampir
-======
-
-Vampir is a commercial trace analysis and visualisation tool. It can
-work with traces in OTF and OTF2 formats. It does not have the
-functionality to collect traces, you need to use a trace collection tool
-(such
-as [Score-P](../../../salomon/software/debuggers/score-p.html))
-first to collect the traces.
-
-
--------------------------------------
-
-Installed versions
-------------------
-
-Version 8.5.0 is currently installed as moduleÂ
-Vampir/8.5.0Â :
-
- $ module load Vampir/8.5.0
- $ vampir &
-
-User manual
------------
-
-You can find the detailed user manual in PDF format inÂ
-$EBROOTVAMPIR/doc/vampir-manual.pdf
-
-References
-----------
-
-1. <https://www.vampir.eu>
-
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-GPI-2
-=====
-
-A library that implements the GASPI specification
-
-
-
-Introduction
-------------
-
-Programming Next Generation Supercomputers: GPI-2 is an API library for
-asynchronous interprocess, cross-node communication. It provides a
-flexible, scalable and fault tolerant interface for parallel
-applications.
-
-The GPI-2 library
-([www.gpi-site.com/gpi2/](http://www.gpi-site.com/gpi2/))
-implements the GASPI specification (Global Address Space Programming
-Interface,
-[www.gaspi.de](http://www.gaspi.de/en/project.html)).
-GASPI is a Partitioned Global Address Space (PGAS) API. It aims at
-scalable, flexible and failure tolerant computing in massively parallel
-environments.
-
-Modules
--------
-
-The GPI-2, version 1.0.2 is available on Anselm via module gpi2:
-
- $ module load gpi2
-
-The module sets up environment variables, required for linking and
-running GPI-2 enabled applications. This particular command loads the
-default module, which is gpi2/1.0.2
-
-Linking
--------
-
-Link with -lGPI2 -libverbs
-
-Load the gpi2 module. Link using **-lGPI2** and *** **-libverbs**
-switches to link your code against GPI-2. The GPI-2 requires the OFED
-infinband communication library ibverbs.
-
-### Compiling and linking with Intel compilers
-
- $ module load intel
- $ module load gpi2
- $ icc myprog.c -o myprog.x -Wl,-rpath=$LIBRARY_PATH -lGPI2 -libverbs
-
-### Compiling and linking with GNU compilers
-
- $ module load gcc
- $ module load gpi2
- $ gcc myprog.c -o myprog.x -Wl,-rpath=$LIBRARY_PATH -lGPI2 -libverbs
-
-Running the GPI-2 codes
------------------------
-
-gaspi_run
-
-gaspi_run starts the GPI-2 application
-
-The gaspi_run utility is used to start and run GPI-2 applications:
-
- $ gaspi_run -m machinefile ./myprog.x
-
-A machine file (**machinefile**) with the hostnames of nodes where the
-application will run, must be provided. The*** machinefile lists all
-nodes on which to run, one entry per node per process. This file may be
-hand created or obtained from standard $PBS_NODEFILE:
-
- $ cut -f1 -d"." $PBS_NODEFILE > machinefile
-
-machinefile:
-
- cn79
- cn80
-
-This machinefile will run 2 GPI-2 processes, one on node cn79 other on
-node cn80.
-
-machinefle:
-
- cn79
- cn79
- cn80
- cn80
-
-This machinefile will run 4 GPI-2 processes, 2 on node cn79 o 2 on node
-cn80.
-
-Use the **mpiprocs** to control how many GPI-2 processes will run per
-node
-
-Example:
-
- $ qsub -A OPEN-0-0 -q qexp -l select=2:ncpus=16:mpiprocs=16 -I
-
-This example will produce $PBS_NODEFILE with 16 entries per node.
-
-### gaspi_logger
-
-gaspi_logger views the output form GPI-2 application ranks
-
-The gaspi_logger utility is used to view the output from all nodes
-except the master node (rank 0). The gaspi_logger is started, on
-another session, on the master node - the node where the gaspi_run is
-executed. The output of the application, when called with
-gaspi_printf(), will be redirected to the gaspi_logger. Other I/O
-routines (e.g. printf) will not.
-
-Example
--------
-
-Following is an example GPI-2 enabled code:
-
- #include <GASPI.h>
- #include <stdlib.h>
-
- void success_or_exit ( const char* file, const int line, const int ec)
- {
- if (ec != GASPI_SUCCESS)
- {
- gaspi_printf ("Assertion failed in %s[%i]:%dn", file, line, ec);
- exit (1);
- }
- }
-
- #define ASSERT(ec) success_or_exit (__FILE__, __LINE__, ec);
-
- int main(int argc, char *argv[])
- {
- gaspi_rank_t rank, num;
- gaspi_return_t ret;
-
- /* Initialize GPI-2 */
- ASSERT( gaspi_proc_init(GASPI_BLOCK) );
-
- /* Get ranks information */
- ASSERT( gaspi_proc_rank(&rank) );
- ASSERT( gaspi_proc_num(&num) );
-
- gaspi_printf("Hello from rank %d of %dn",
- rank, num);
-
- /* Terminate */
- ASSERT( gaspi_proc_term(GASPI_BLOCK) );
-
- return 0;
- }
-
-Load modules and compile:
-
- $ module load gcc gpi2
- $ gcc helloworld_gpi.c -o helloworld_gpi.x -Wl,-rpath=$LIBRARY_PATH -lGPI2 -libverbs
-
-Submit the job and run the GPI-2 application
-
- $ qsub -q qexp -l select=2:ncpus=1:mpiprocs=1,place=scatter,walltime=00:05:00 -I
- qsub: waiting for job 171247.dm2 to start
- qsub: job 171247.dm2 ready
-
- cn79 $ module load gpi2
- cn79 $ cut -f1 -d"." $PBS_NODEFILE > machinefile
- cn79 $ gaspi_run -m machinefile ./helloworld_gpi.x
- Hello from rank 0 of 2
-
-At the same time, in another session, you may start the gaspi logger:
-
- $ ssh cn79
- cn79 $ gaspi_logger
- GASPI Logger (v1.1)
- [cn80:0] Hello from rank 1 of 2
-
-In this example, we compile the helloworld_gpi.c code using the **gnu
-compiler**Â (gcc) and link it to the GPI-2 and ibverbs library. The
-library search path is compiled in. For execution, we use the qexp
-queue, 2 nodes 1 core each. The GPI module must be loaded on the master
-compute node (in this example the cn79), gaspi_logger is used from
-different session to view the output of the second process.
-
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-Intel Parallel Studio
-=====================
-
-
-
-The Anselm cluster provides following elements of the Intel Parallel
-Studio XE
-
- Intel Parallel Studio XE
- -------------------------------------------------
- Intel Compilers
- Intel Debugger
- Intel MKL Library
- Intel Integrated Performance Primitives Library
- Intel Threading Building Blocks Library
-
-Intel compilers
----------------
-
-The Intel compilers version 13.1.3 are available, via module intel. The
-compilers include the icc C and C++ compiler and the ifort fortran
-77/90/95 compiler.
-
- $ module load intel
- $ icc -v
- $ ifort -v
-
-Read more at the [Intel
-Compilers](intel-suite/intel-compilers.html) page.
-
-Intel debugger
---------------
-
-Â The intel debugger version 13.0 is available, via module intel. The
-debugger works for applications compiled with C and C++ compiler and the
-ifort fortran 77/90/95 compiler. The debugger provides java GUI
-environment. Use [X
-display](https://docs.it4i.cz/anselm-cluster-documentation/software/intel-suite/resolveuid/11e53ad0d2fd4c5187537f4baeedff33)
-for running the GUI.
-
- $ module load intel
- $ idb
-
-Read more at the [Intel
-Debugger](intel-suite/intel-debugger.html) page.
-
-Intel Math Kernel Library
--------------------------
-
-Intel Math Kernel Library (Intel MKL) is a library of math kernel
-subroutines, extensively threaded and optimized for maximum performance.
-Intel MKL unites and provides these basic components: BLAS, LAPACK,
-ScaLapack, PARDISO, FFT, VML, VSL, Data fitting, Feast Eigensolver and
-many more.
-
- $ module load mkl
-
-Read more at the [Intel MKL](intel-suite/intel-mkl.html)
-page.
-
-Intel Integrated Performance Primitives
----------------------------------------
-
-Intel Integrated Performance Primitives, version 7.1.1, compiled for AVX
-is available, via module ipp. The IPP is a library of highly optimized
-algorithmic building blocks for media and data applications. This
-includes signal, image and frame processing algorithms, such as FFT,
-FIR, Convolution, Optical Flow, Hough transform, Sum, MinMax and many
-more.
-
- $ module load ipp
-
-Read more at the [Intel
-IPP](intel-suite/intel-integrated-performance-primitives.html)
-page.
-
-Intel Threading Building Blocks
--------------------------------
-
-Intel Threading Building Blocks (Intel TBB) is a library that supports
-scalable parallel programming using standard ISO C++ code. It does not
-require special languages or compilers. It is designed to promote
-scalable data parallel programming. Additionally, it fully supports
-nested parallelism, so you can build larger parallel components from
-smaller parallel components. To use the library, you specify tasks, not
-threads, and let the library map tasks onto threads in an efficient
-manner.
-
- $ module load tbb
-
-Read more at the [Intel TBB](intel-suite/intel-tbb.html)
-page.
-
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-Intel Compilers
-===============
-
-
-
-The Intel compilers version 13.1.1 are available, via module intel. The
-compilers include the icc C and C++ compiler and the ifort fortran
-77/90/95 compiler.
-
- $ module load intel
- $ icc -v
- $ ifort -v
-
-The intel compilers provide for vectorization of the code, via the AVX
-instructions and support threading parallelization via OpenMP
-
-For maximum performance on the Anselm cluster, compile your programs
-using the AVX instructions, with reporting where the vectorization was
-used. We recommend following compilation options for high performance
-
- $ icc -ipo -O3 -vec -xAVX -vec-report1 myprog.c mysubroutines.c -o myprog.x
- $ ifort -ipo -O3 -vec -xAVX -vec-report1 myprog.f mysubroutines.f -o myprog.x
-
-In this example, we compile the program enabling interprocedural
-optimizations between source files (-ipo), aggresive loop optimizations
-(-O3) and vectorization (-vec -xAVX)
-
-The compiler recognizes the omp, simd, vector and ivdep pragmas for
-OpenMP parallelization and AVX vectorization. Enable the OpenMP
-parallelization by the **-openmp** compiler switch.
-
- $ icc -ipo -O3 -vec -xAVX -vec-report1 -openmp myprog.c mysubroutines.c -o myprog.x
- $ ifort -ipo -O3 -vec -xAVX -vec-report1 -openmp myprog.f mysubroutines.f -o myprog.x
-
-Read more at
-<http://software.intel.com/sites/products/documentation/doclib/stdxe/2013/composerxe/compiler/cpp-lin/index.htm>
-
-Sandy Bridge/Haswell binary compatibility
------------------------------------------
-
-Anselm nodes are currently equipped with Sandy Bridge CPUs, while
-Salomon will use Haswell architecture. >The new processors are
-backward compatible with the Sandy Bridge nodes, so all programs that
-ran on the Sandy Bridge processors, should also run on the new Haswell
-nodes. >To get optimal performance out of the Haswell
-processors a program should make use of the special >AVX2
-instructions for this processor. One can do this by recompiling codes
-with the compiler flags >designated to invoke these
-instructions. For the Intel compiler suite, there are two ways of
-doing >this:
-
-- >Using compiler flag (both for Fortran and C):
- -xCORE-AVX2. This will create a
- binary class="s1">with AVX2 instructions, specifically
- for the Haswell processors. Note that the
- executable >will not run on Sandy Bridge nodes.
-- >Using compiler flags (both for Fortran and C):
- -xAVX -axCORE-AVX2. This
- will >generate multiple, feature specific auto-dispatch
- code paths for Intel® processors, if there is >a
- performance benefit. So this binary will run both on Sandy Bridge
- and Haswell >processors. During runtime it will be
- decided which path to follow, dependent on
- which >processor you are running on. In general this
- will result in larger binaries.
-
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-Intel Debugger
-==============
-
-
-
-Debugging serial applications
------------------------------
-
-Â The intel debugger version 13.0 is available, via module intel. The
-debugger works for applications compiled with C and C++ compiler and the
-ifort fortran 77/90/95 compiler. The debugger provides java GUI
-environment. Use [X
-display](https://docs.it4i.cz/anselm-cluster-documentation/software/intel-suite/resolveuid/11e53ad0d2fd4c5187537f4baeedff33)
-for running the GUI.
-
- $ module load intel
- $ idb
-
-The debugger may run in text mode. To debug in text mode, use
-
- $ idbc
-
-To debug on the compute nodes, module intel must be loaded.
-The GUI on compute nodes may be accessed using the same way as in [the
-GUI
-section](https://docs.it4i.cz/anselm-cluster-documentation/software/intel-suite/resolveuid/11e53ad0d2fd4c5187537f4baeedff33)
-
-Example:
-
- $ qsub -q qexp -l select=1:ncpus=16 -X -I
- qsub: waiting for job 19654.srv11 to start
- qsub: job 19654.srv11 ready
-
- $ module load intel
- $ module load java
- $ icc -O0 -g myprog.c -o myprog.x
- $ idb ./myprog.x
-
-In this example, we allocate 1 full compute node, compile program
-myprog.c with debugging options -O0 -g and run the idb debugger
-interactively on the myprog.x executable. The GUI access is via X11 port
-forwarding provided by the PBS workload manager.
-
-Debugging parallel applications
--------------------------------
-
-Intel debugger is capable of debugging multithreaded and MPI parallel
-programs as well.
-
-### Small number of MPI ranks
-
-For debugging small number of MPI ranks, you may execute and debug each
-rank in separate xterm terminal (do not forget the [X
-display](https://docs.it4i.cz/anselm-cluster-documentation/software/intel-suite/resolveuid/11e53ad0d2fd4c5187537f4baeedff33)).
-Using Intel MPI, this may be done in following way:
-
- $ qsub -q qexp -l select=2:ncpus=16 -X -I
- qsub: waiting for job 19654.srv11 to start
- qsub: job 19655.srv11 ready
-
- $ module load intel impi
- $ mpirun -ppn 1 -hostfile $PBS_NODEFILE --enable-x xterm -e idbc ./mympiprog.x
-
-In this example, we allocate 2 full compute node, run xterm on each node
-and start idb debugger in command line mode, debugging two ranks of
-mympiprog.x application. The xterm will pop up for each rank, with idb
-prompt ready. The example is not limited to use of Intel MPI
-
-### Large number of MPI ranks
-
-Run the idb debugger from within the MPI debug option. This will cause
-the debugger to bind to all ranks and provide aggregated outputs across
-the ranks, pausing execution automatically just after startup. You may
-then set break points and step the execution manually. Using Intel MPI:
-
- $ qsub -q qexp -l select=2:ncpus=16 -X -I
- qsub: waiting for job 19654.srv11 to start
- qsub: job 19655.srv11 ready
-
- $ module load intel impi
- $ mpirun -n 32 -idb ./mympiprog.x
-
-### Debugging multithreaded application
-
-Run the idb debugger in GUI mode. The menu Parallel contains number of
-tools for debugging multiple threads. One of the most useful tools is
-the **Serialize Execution** tool, which serializes execution of
-concurrent threads for easy orientation and identification of
-concurrency related bugs.
-
-Further information
--------------------
-
-Exhaustive manual on idb features and usage is published at Intel
-website,
-<http://software.intel.com/sites/products/documentation/doclib/stdxe/2013/composerxe/debugger/user_guide/index.htm>
-
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-Intel IPP
-=========
-
-
-
-Intel Integrated Performance Primitives
----------------------------------------
-
-Intel Integrated Performance Primitives, version 7.1.1, compiled for AVX
-vector instructions is available, via module ipp. The IPP is a very rich
-library of highly optimized algorithmic building blocks for media and
-data applications. This includes signal, image and frame processing
-algorithms, such as FFT, FIR, Convolution, Optical Flow, Hough
-transform, Sum, MinMax, as well as cryptographic functions, linear
-algebra functions and many more.
-
-Check out IPP before implementing own math functions for data
-processing, it is likely already there.
-
- $ module load ipp
-
-The module sets up environment variables, required for linking and
-running ipp enabled applications.
-
-IPP example
------------
-
- #include "ipp.h"
- #include <stdio.h>
- int main(int argc, char* argv[])
- {
- const IppLibraryVersion *lib;
- Ipp64u fm;
- IppStatus status;
-
- status= ippInit(); //IPP initialization with the best optimization layer
- if( status != ippStsNoErr ) {
- printf("IppInit() Error:n");
- printf("%sn", ippGetStatusString(status) );
- return -1;
- }
-
- //Get version info
- lib = ippiGetLibVersion();
- printf("%s %sn", lib->Name, lib->Version);
-
- //Get CPU features enabled with selected library level
- fm=ippGetEnabledCpuFeatures();
- printf("SSE :%cn",(fm>1)&1?'Y':'N');
- printf("SSE2 :%cn",(fm>2)&1?'Y':'N');
- printf("SSE3 :%cn",(fm>3)&1?'Y':'N');
- printf("SSSE3 :%cn",(fm>4)&1?'Y':'N');
- printf("SSE41 :%cn",(fm>6)&1?'Y':'N');
- printf("SSE42 :%cn",(fm>7)&1?'Y':'N');
- printf("AVX :%cn",(fm>8)&1 ?'Y':'N');
- printf("AVX2 :%cn", (fm>15)&1 ?'Y':'N' );
- printf("----------n");
- printf("OS Enabled AVX :%cn", (fm>9)&1 ?'Y':'N');
- printf("AES :%cn", (fm>10)&1?'Y':'N');
- printf("CLMUL :%cn", (fm>11)&1?'Y':'N');
- printf("RDRAND :%cn", (fm>13)&1?'Y':'N');
- printf("F16C :%cn", (fm>14)&1?'Y':'N');
-
- return 0;
- }
-
-Â Compile above example, using any compiler and the ipp module.
-
- $ module load intel
- $ module load ipp
-
- $ icc testipp.c -o testipp.x -lippi -lipps -lippcore
-
-You will need the ipp module loaded to run the ipp enabled executable.
-This may be avoided, by compiling library search paths into the
-executable
-
- $ module load intel
- $ module load ipp
-
- $ icc testipp.c -o testipp.x -Wl,-rpath=$LIBRARY_PATH -lippi -lipps -lippcore
-
-Code samples and documentation
-------------------------------
-
-Intel provides number of [Code Samples for
-IPP](https://software.intel.com/en-us/articles/code-samples-for-intel-integrated-performance-primitives-library),
-illustrating use of IPP.
-
-Read full documentation on IPP [on Intel
-website,](http://software.intel.com/sites/products/search/search.php?q=&x=15&y=6&product=ipp&version=7.1&docos=lin)
-in particular the [IPP Reference
-manual.](http://software.intel.com/sites/products/documentation/doclib/ipp_sa/71/ipp_manual/index.htm)
-
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-Intel MKL
-=========
-
-
-
-Intel Math Kernel Library
--------------------------
-
-Intel Math Kernel Library (Intel MKL) is a library of math kernel
-subroutines, extensively threaded and optimized for maximum performance.
-Intel MKL provides these basic math kernels:
-
--
-
-
-
- BLAS (level 1, 2, and 3) and LAPACK linear algebra routines,
- offering vector, vector-matrix, and matrix-matrix operations.
--
-
-
-
- The PARDISO direct sparse solver, an iterative sparse solver,
- and supporting sparse BLAS (level 1, 2, and 3) routines for solving
- sparse systems of equations.
--
-
-
-
- ScaLAPACK distributed processing linear algebra routines for
- Linux* and Windows* operating systems, as well as the Basic Linear
- Algebra Communications Subprograms (BLACS) and the Parallel Basic
- Linear Algebra Subprograms (PBLAS).
--
-
-
-
- Fast Fourier transform (FFT) functions in one, two, or three
- dimensions with support for mixed radices (not limited to sizes that
- are powers of 2), as well as distributed versions of
- these functions.
--
-
-
-
- Vector Math Library (VML) routines for optimized mathematical
- operations on vectors.
--
-
-
-
- Vector Statistical Library (VSL) routines, which offer
- high-performance vectorized random number generators (RNG) for
- several probability distributions, convolution and correlation
- routines, and summary statistics functions.
--
-
-
-
- Data Fitting Library, which provides capabilities for
- spline-based approximation of functions, derivatives and integrals
- of functions, and search.
-- Extended Eigensolver, a shared memory version of an eigensolver
- based on the Feast Eigenvalue Solver.
-
-For details see the [Intel MKL Reference
-Manual](http://software.intel.com/sites/products/documentation/doclib/mkl_sa/11/mklman/index.htm).
-
-Intel MKL version 13.5.192 is available on Anselm
-
- $ module load mkl
-
-The module sets up environment variables, required for linking and
-running mkl enabled applications. The most important variables are the
-$MKLROOT, $MKL_INC_DIR, $MKL_LIB_DIR and $MKL_EXAMPLES
-
-The MKL library may be linked using any compiler.
-With intel compiler use -mkl option to link default threaded MKL.
-
-### Interfaces
-
-The MKL library provides number of interfaces. The fundamental once are
-the LP64 and ILP64. The Intel MKL ILP64 libraries use the 64-bit integer
-type (necessary for indexing large arrays, with more than 231^-1
-elements), whereas the LP64 libraries index arrays with the 32-bit
-integer type.
-
- |Interface|Integer type|
- ----- |---|---|-------------------------------------
- |LP64|32-bit, int, integer(kind=4), MPI_INT|
- ILP64 64-bit, long int, integer(kind=8), MPI_INT64
-
-### Linking
-
-Linking MKL libraries may be complex. Intel [mkl link line
-advisor](http://software.intel.com/en-us/articles/intel-mkl-link-line-advisor)
-helps. See also [examples](intel-mkl.html#examples) below.
-
-You will need the mkl module loaded to run the mkl enabled executable.
-This may be avoided, by compiling library search paths into the
-executable. Include rpath on the compile line:
-
- $ icc .... -Wl,-rpath=$LIBRARY_PATH ...
-
-### Threading
-
-Advantage in using the MKL library is that it brings threaded
-parallelization to applications that are otherwise not parallel.
-
-For this to work, the application must link the threaded MKL library
-(default). Number and behaviour of MKL threads may be controlled via the
-OpenMP environment variables, such as OMP_NUM_THREADS and
-KMP_AFFINITY. MKL_NUM_THREADS takes precedence over OMP_NUM_THREADS
-
- $ export OMP_NUM_THREADS=16
- $ export KMP_AFFINITY=granularity=fine,compact,1,0
-
-The application will run with 16 threads with affinity optimized for
-fine grain parallelization.
-
-Examples
-------------
-
-Number of examples, demonstrating use of the MKL library and its linking
-is available on Anselm, in the $MKL_EXAMPLES directory. In the
-examples below, we demonstrate linking MKL to Intel and GNU compiled
-program for multi-threaded matrix multiplication.
-
-### Working with examples
-
- $ module load intel
- $ module load mkl
- $ cp -a $MKL_EXAMPLES/cblas /tmp/
- $ cd /tmp/cblas
-
- $ make sointel64 function=cblas_dgemm
-
-In this example, we compile, link and run the cblas_dgemm example,
-demonstrating use of MKL example suite installed on Anselm.
-
-### Example: MKL and Intel compiler
-
- $ module load intel
- $ module load mkl
- $ cp -a $MKL_EXAMPLES/cblas /tmp/
- $ cd /tmp/cblas
- $
- $ icc -w source/cblas_dgemmx.c source/common_func.c -mkl -o cblas_dgemmx.x
- $ ./cblas_dgemmx.x data/cblas_dgemmx.d
-
-In this example, we compile, link and run the cblas_dgemm example,
-demonstrating use of MKL with icc -mkl option. Using the -mkl option is
-equivalent to:
-
- $ icc -w source/cblas_dgemmx.c source/common_func.c -o cblas_dgemmx.x
- -I$MKL_INC_DIR -L$MKL_LIB_DIR -lmkl_intel_lp64 -lmkl_intel_thread -lmkl_core -liomp5
-
-In this example, we compile and link the cblas_dgemm example, using
-LP64 interface to threaded MKL and Intel OMP threads implementation.
-
-### Example: MKL and GNU compiler
-
- $ module load gcc
- $ module load mkl
- $ cp -a $MKL_EXAMPLES/cblas /tmp/
- $ cd /tmp/cblas
-
- $ gcc -w source/cblas_dgemmx.c source/common_func.c -o cblas_dgemmx.x
- -lmkl_intel_lp64 -lmkl_gnu_thread -lmkl_core -lgomp -lm
-
- $ ./cblas_dgemmx.x data/cblas_dgemmx.d
-
-In this example, we compile, link and run the cblas_dgemm example,
-using LP64 interface to threaded MKL and gnu OMP threads implementation.
-
-MKL and MIC accelerators
-------------------------
-
-The MKL is capable to automatically offload the computations o the MIC
-accelerator. See section [Intel Xeon
-Phi](../intel-xeon-phi.html) for details.
-
-Further reading
----------------
-
-Read more on [Intel
-website](http://software.intel.com/en-us/intel-mkl), in
-particular the [MKL users
-guide](https://software.intel.com/en-us/intel-mkl/documentation/linux).
-
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-Intel Parallel Studio
-=====================
-
-
-
-The Anselm cluster provides following elements of the Intel Parallel
-Studio XE
-
- Intel Parallel Studio XE
- -------------------------------------------------
- Intel Compilers
- Intel Debugger
- Intel MKL Library
- Intel Integrated Performance Primitives Library
- Intel Threading Building Blocks Library
-
-Intel compilers
----------------
-
-The Intel compilers version 13.1.3 are available, via module intel. The
-compilers include the icc C and C++ compiler and the ifort fortran
-77/90/95 compiler.
-
- $ module load intel
- $ icc -v
- $ ifort -v
-
-Read more at the [Intel Compilers](intel-compilers.html)
-page.
-
-Intel debugger
---------------
-
-Â The intel debugger version 13.0 is available, via module intel. The
-debugger works for applications compiled with C and C++ compiler and the
-ifort fortran 77/90/95 compiler. The debugger provides java GUI
-environment. Use [X
-display](https://docs.it4i.cz/anselm-cluster-documentation/software/intel-suite/resolveuid/11e53ad0d2fd4c5187537f4baeedff33)
-for running the GUI.
-
- $ module load intel
- $ idb
-
-Read more at the [Intel Debugger](intel-debugger.html)
-page.
-
-Intel Math Kernel Library
--------------------------
-
-Intel Math Kernel Library (Intel MKL) is a library of math kernel
-subroutines, extensively threaded and optimized for maximum performance.
-Intel MKL unites and provides these basic components: BLAS, LAPACK,
-ScaLapack, PARDISO, FFT, VML, VSL, Data fitting, Feast Eigensolver and
-many more.
-
- $ module load mkl
-
-Read more at the [Intel MKL](intel-mkl.html) page.
-
-Intel Integrated Performance Primitives
----------------------------------------
-
-Intel Integrated Performance Primitives, version 7.1.1, compiled for AVX
-is available, via module ipp. The IPP is a library of highly optimized
-algorithmic building blocks for media and data applications. This
-includes signal, image and frame processing algorithms, such as FFT,
-FIR, Convolution, Optical Flow, Hough transform, Sum, MinMax and many
-more.
-
- $ module load ipp
-
-Read more at the [Intel
-IPP](intel-integrated-performance-primitives.html) page.
-
-Intel Threading Building Blocks
--------------------------------
-
-Intel Threading Building Blocks (Intel TBB) is a library that supports
-scalable parallel programming using standard ISO C++ code. It does not
-require special languages or compilers. It is designed to promote
-scalable data parallel programming. Additionally, it fully supports
-nested parallelism, so you can build larger parallel components from
-smaller parallel components. To use the library, you specify tasks, not
-threads, and let the library map tasks onto threads in an efficient
-manner.
-
- $ module load tbb
-
-Read more at the [Intel TBB](intel-tbb.html) page.
-
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-Intel TBB
-=========
-
-
-
-Intel Threading Building Blocks
--------------------------------
-
-Intel Threading Building Blocks (Intel TBB) is a library that supports
-scalable parallel programming using standard ISO C++ code. It does not
-require special languages or compilers. To use the library, you specify
-tasks, not threads, and let the library map tasks onto threads in an
-efficient manner. The tasks are executed by a runtime scheduler and may
-be offloaded to [MIC
-accelerator](../intel-xeon-phi.html).
-
-Intel TBB version 4.1 is available on Anselm
-
- $ module load tbb
-
-The module sets up environment variables, required for linking and
-running tbb enabled applications.
-
-Link the tbb library, using -ltbb
-
-Examples
---------
-
-Number of examples, demonstrating use of TBB and its built-in schedulerÂ
-is available on Anselm, in the $TBB_EXAMPLES directory.
-
- $ module load intel
- $ module load tbb
- $ cp -a $TBB_EXAMPLES/common $TBB_EXAMPLES/parallel_reduce /tmp/
- $ cd /tmp/parallel_reduce/primes
- $ icc -O2 -DNDEBUG -o primes.x main.cpp primes.cpp -ltbb
- $ ./primes.x
-
-In this example, we compile, link and run the primes example,
-demonstrating use of parallel task-based reduce in computation of prime
-numbers.
-
-You will need the tbb module loaded to run the tbb enabled executable.
-This may be avoided, by compiling library search paths into the
-executable.
-
- $ icc -O2 -o primes.x main.cpp primes.cpp -Wl,-rpath=$LIBRARY_PATH -ltbb
-
-Further reading
----------------
-
-Read more on Intel website,
-<http://software.intel.com/sites/products/documentation/doclib/tbb_sa/help/index.htm>
-
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-Intel Xeon Phi
-==============
-
-A guide to Intel Xeon Phi usage
-
-
-
-Intel Xeon Phi can be programmed in several modes. The default mode on
-Anselm is offload mode, but all modes described in this document are
-supported.
-
-Intel Utilities for Xeon Phi
-----------------------------
-
-To get access to a compute node with Intel Xeon Phi accelerator, use the
-PBS interactive session
-
- $ qsub -I -q qmic -A NONE-0-0
-
-To set up the environment module "Intel" has to be loaded
-
- $ module load intel/13.5.192
-
-Information about the hardware can be obtained by running
-the micinfo program on the host.
-
- $ /usr/bin/micinfo
-
-The output of the "micinfo" utility executed on one of the Anselm node
-is as follows. (note: to get PCIe related details the command has to be
-run with root privileges)
-
- MicInfo Utility Log
-
- Created Mon Jul 22 00:23:50 2013
-
- Â Â Â Â Â Â Â System Info
- Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â HOST OSÂ Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â : Linux
-                OS Version             : 2.6.32-279.5.2.bl6.Bull.33.x86_64
-                Driver Version         : 6720-15
-                MPSS Version           : 2.1.6720-15
-                Host Physical Memory   : 98843 MB
-
- Device No: 0, Device Name: mic0
-
- Â Â Â Â Â Â Â Version
-                Flash Version           : 2.1.03.0386
-                SMC Firmware Version    : 1.15.4830
-                SMC Boot Loader Version : 1.8.4326
-                uOS Version             : 2.6.38.8-g2593b11
-                Device Serial Number    : ADKC30102482
-
- Â Â Â Â Â Â Â Board
- Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Vendor IDÂ Â Â Â Â Â Â Â Â Â Â Â Â Â Â : 0x8086
- Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Device IDÂ Â Â Â Â Â Â Â Â Â Â Â Â Â Â : 0x2250
- Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Subsystem IDÂ Â Â Â Â Â Â Â Â Â Â Â : 0x2500
- Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Coprocessor Stepping IDÂ : 3
-                PCIe Width              : x16
-                PCIe Speed              : 5 GT/s
-                PCIe Max payload size   : 256 bytes
-                PCIe Max read req size  : 512 bytes
-                Coprocessor Model       : 0x01
-                Coprocessor Model Ext   : 0x00
-                Coprocessor Type        : 0x00
-                Coprocessor Family      : 0x0b
-                Coprocessor Family Ext  : 0x00
-                Coprocessor Stepping    : B1
- Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Board SKUÂ Â Â Â Â Â Â Â Â Â Â Â Â Â Â : B1PRQ-5110P/5120D
-                ECC Mode                : Enabled
-                SMC HW Revision         : Product 225W Passive CS
-
- Â Â Â Â Â Â Â Cores
- Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Total No of Active Cores : 60
-                Voltage                 : 1032000 uV
-                Frequency               : 1052631 kHz
-
- Â Â Â Â Â Â Â Thermal
-                Fan Speed Control       : N/A
- Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Fan RPMÂ Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â : N/A
- Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Fan PWMÂ Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â : N/A
-                Die Temp                : 49 C
-
- Â Â Â Â Â Â Â GDDR
-                GDDR Vendor             : Elpida
-                GDDR Version            : 0x1
-                GDDR Density            : 2048 Mb
-                GDDR Size               : 7936 MB
-                GDDR Technology         : GDDR5
-                GDDR Speed              : 5.000000 GT/s
-                GDDR Frequency          : 2500000 kHz
-                GDDR Voltage            : 1501000 uV
-
-Offload Mode
-------------
-
-To compile a code for Intel Xeon Phi a MPSS stack has to be installed on
-the machine where compilation is executed. Currently the MPSS stack is
-only installed on compute nodes equipped with accelerators.
-
- $ qsub -I -q qmic -A NONE-0-0
- $ module load intel/13.5.192
-
-For debugging purposes it is also recommended to set environment
-variable "OFFLOAD_REPORT". Value can be set from 0 to 3, where higher
-number means more debugging information.
-
- export OFFLOAD_REPORT=3
-
-A very basic example of code that employs offload programming technique
-is shown in the next listing. Please note that this code is sequential
-and utilizes only single core of the accelerator.
-
- $ vim source-offload.cpp
-
- #include <iostream>
-
- int main(int argc, char* argv[])
- {
- Â Â Â const int niter = 100000;
- Â Â Â double result = 0;
-
- Â #pragma offload target(mic)
- Â Â Â for (int i = 0; i < niter; ++i) {
- Â Â Â Â Â Â Â const double t = (i + 0.5) / niter;
- Â Â Â Â Â Â Â result += 4.0 / (t * t + 1.0);
- Â Â Â }
- Â Â Â result /= niter;
- Â Â Â std::cout << "Pi ~ " << result << 'n';
- }
-
-To compile a code using Intel compiler run
-
- $ icc source-offload.cpp -o bin-offload
-
-To execute the code, run the following command on the host
-
- ./bin-offload
-
-### Parallelization in Offload Mode Using OpenMP
-
-One way of paralelization a code for Xeon Phi is using OpenMP
-directives. The following example shows code for parallel vector
-addition.Â
-
- $ vim ./vect-add
-
- #include <stdio.h>
-
- typedef int T;
-
- #define SIZE 1000
-
- #pragma offload_attribute(push, target(mic))
- T in1[SIZE];
- T in2[SIZE];
- T res[SIZE];
- #pragma offload_attribute(pop)
-
- // MIC function to add two vectors
- __attribute__((target(mic))) add_mic(T *a, T *b, T *c, int size) {
- Â int i = 0;
- Â #pragma omp parallel for
- Â Â Â for (i = 0; i < size; i++)
- Â Â Â Â Â c[i] = a[i] + b[i];
- }
-
- // CPU function to add two vectors
- void add_cpu (T *a, T *b, T *c, int size) {
- Â int i;
- Â for (i = 0; i < size; i++)
- Â Â Â c[i] = a[i] + b[i];
- }
-
- // CPU function to generate a vector of random numbers
- void random_T (T *a, int size) {
- Â int i;
- Â for (i = 0; i < size; i++)
- Â Â Â a[i] = rand() % 10000; // random number between 0 and 9999
- }
-
- // CPU function to compare two vectors
- int compare(T *a, T *b, T size ){
- Â int pass = 0;
- Â int i;
- Â for (i = 0; i < size; i++){
- Â Â Â if (a[i] != b[i]) {
- Â Â Â Â Â printf("Value mismatch at location %d, values %d and %dn",i, a[i], b[i]);
- Â Â Â Â Â pass = 1;
- Â Â Â }
- Â }
- Â if (pass == 0) printf ("Test passedn"); else printf ("Test Failedn");
- Â return pass;
- }
-
- int main()
- {
- Â int i;
- Â random_T(in1, SIZE);
- Â random_T(in2, SIZE);
-
- Â #pragma offload target(mic) in(in1,in2)Â inout(res)
- Â {
-
- Â Â Â // Parallel loop from main function
- Â Â Â #pragma omp parallel for
- Â Â Â for (i=0; i<SIZE; i++)
- Â Â Â Â Â res[i] = in1[i] + in2[i];
-
- Â Â Â // or parallel loop is called inside the function
- Â Â Â add_mic(in1, in2, res, SIZE);
-
- Â }
-
- Â //Check the results with CPU implementation
- Â T res_cpu[SIZE];
- Â add_cpu(in1, in2, res_cpu, SIZE);
- Â compare(res, res_cpu, SIZE);
-
- }
-
-During the compilation Intel compiler shows which loops have been
-vectorized in both host and accelerator. This can be enabled with
-compiler option "-vec-report2". To compile and execute the code run
-
- $ icc vect-add.c -openmp_report2 -vec-report2 -o vect-add
-
- $ ./vect-add
-
-Some interesting compiler flags useful not only for code debugging are:
-
-Debugging
-Â openmp_report[0|1|2] - controls the compiler based vectorization
-diagnostic level
-Â vec-report[0|1|2] - controls the OpenMP parallelizer diagnostic
-level
-
-Performance ooptimization
-Â xhost - FOR HOST ONLY - to generate AVX (Advanced Vector Extensions)
-instructions.
-
-Automatic Offload using Intel MKL Library
------------------------------------------
-
-Intel MKL includes an Automatic Offload (AO) feature that enables
-computationally intensive MKL functions called in user code to benefit
-from attached Intel Xeon Phi coprocessors automatically and
-transparently.
-
-Behavioral of automatic offload mode is controlled by functions called
-within the program or by environmental variables. Complete list of
-controls is listed [
-here](http://software.intel.com/sites/products/documentation/doclib/mkl_sa/11/mkl_userguide_lnx/GUID-3DC4FC7D-A1E4-423D-9C0C-06AB265FFA86.htm).
-
-The Automatic Offload may be enabled by either an MKL function call
-within the code:
-
- mkl_mic_enable();
-
-or by setting environment variable
-
- $ export MKL_MIC_ENABLE=1
-
-To get more information about automatic offload please refer to "[Using
-Intel® MKL Automatic Offload on Intel ® Xeon Phi™
-Coprocessors](http://software.intel.com/sites/default/files/11MIC42_How_to_Use_MKL_Automatic_Offload_0.pdf)"
-white paper or [ Intel MKL
-documentation](https://software.intel.com/en-us/articles/intel-math-kernel-library-documentation).
-
-### Automatic offload example
-
-At first get an interactive PBS session on a node with MIC accelerator
-and load "intel" module that automatically loads "mkl" module as well.
-
- $ qsub -I -q qmic -A OPEN-0-0 -l select=1:ncpus=16
- $ module load intel
-
-Following example show how to automatically offload an SGEMM (single
-precision - g dir="auto">eneral matrix multiply) function to
-MIC coprocessor. The code can be copied to a file and compiled without
-any necessary modification.Â
-
- $ vim sgemm-ao-short.c
-
- #include <stdio.h>
- #include <stdlib.h>
- #include <malloc.h>
- #include <stdint.h>
-
- #include "mkl.h"
-
- int main(int argc, char **argv)
- {
- Â Â Â Â Â Â Â float *A, *B, *C; /* Matrices */
-
- Â Â Â Â Â Â Â MKL_INT N = 2560; /* Matrix dimensions */
- Â Â Â Â Â Â Â MKL_INT LD = N; /* Leading dimension */
- Â Â Â Â Â Â Â int matrix_bytes; /* Matrix size in bytes */
- Â Â Â Â Â Â Â int matrix_elements; /* Matrix size in elements */
-
- Â Â Â Â Â Â Â float alpha = 1.0, beta = 1.0; /* Scaling factors */
- Â Â Â Â Â Â Â char transa = 'N', transb = 'N'; /* Transposition options */
-
- Â Â Â Â Â Â Â int i, j; /* Counters */
-
- Â Â Â Â Â Â Â matrix_elements = N * N;
- Â Â Â Â Â Â Â matrix_bytes = sizeof(float) * matrix_elements;
-
- Â Â Â Â Â Â Â /* Allocate the matrices */
- Â Â Â Â Â Â Â A = malloc(matrix_bytes); B = malloc(matrix_bytes); C = malloc(matrix_bytes);
-
- Â Â Â Â Â Â Â /* Initialize the matrices */
- Â Â Â Â Â Â Â for (i = 0; i < matrix_elements; i++) {
- Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â A[i] = 1.0; B[i] = 2.0; C[i] = 0.0;
- Â Â Â Â Â Â Â }
-
- Â Â Â Â Â Â Â printf("Computing SGEMM on the hostn");
- Â Â Â Â Â Â Â sgemm(&transa, &transb, &N, &N, &N, &alpha, A, &N, B, &N, &beta, C, &N);
-
- Â Â Â Â Â Â Â printf("Enabling Automatic Offloadn");
- Â Â Â Â Â Â Â /* Alternatively, set environment variable MKL_MIC_ENABLE=1 */
- Â Â Â Â Â Â Â mkl_mic_enable();
- Â Â Â Â Â Â Â
- Â Â Â Â Â Â Â int ndevices = mkl_mic_get_device_count(); /* Number of MIC devices */
-        printf("Automatic Offload enabled: %d MIC devices presentn",  ndevices);
-
- Â Â Â Â Â Â Â printf("Computing SGEMM with automatic workdivisionn");
- Â Â Â Â Â Â Â sgemm(&transa, &transb, &N, &N, &N, &alpha, A, &N, B, &N, &beta, C, &N);
-
- Â Â Â Â Â Â Â /* Free the matrix memory */
- Â Â Â Â Â Â Â free(A); free(B); free(C);
-
- Â Â Â Â Â Â Â printf("Donen");
-
- Â Â Â return 0;
- }
-
-Please note: This example is simplified version of an example from MKL.
-The expanded version can be found here:
-$MKL_EXAMPLES/mic_ao/blasc/source/sgemm.c**
-
-To compile a code using Intel compiler use:
-
- $ icc -mkl sgemm-ao-short.c -o sgemm
-
-For debugging purposes enable the offload report to see more information
-about automatic offloading.
-
- $ export OFFLOAD_REPORT=2
-
-The output of a code should look similar to following listing, where
-lines starting with [MKL] are generated by offload reporting:
-
- Computing SGEMM on the host
- Enabling Automatic Offload
- Automatic Offload enabled: 1 MIC devices present
- Computing SGEMM with automatic workdivision
- [MKL] [MIC --] [AO Function]Â Â Â SGEMM
- [MKL] [MIC --] [AO SGEMM Workdivision]Â 0.00 1.00
- [MKL] [MIC 00] [AO SGEMM CPU Time]Â Â Â Â Â 0.463351 seconds
- [MKL] [MIC 00] [AO SGEMM MIC Time]Â Â Â Â Â 0.179608 seconds
- [MKL] [MIC 00] [AO SGEMM CPU->MIC Data] 52428800 bytes
- [MKL] [MIC 00] [AO SGEMM MIC->CPU Data] 26214400 bytes
- Done
-
-Â
-
-Native Mode
------------
-
-In the native mode a program is executed directly on Intel Xeon Phi
-without involvement of the host machine. Similarly to offload mode, the
-code is compiled on the host computer with Intel compilers.
-
-To compile a code user has to be connected to a compute with MIC and
-load Intel compilers module. To get an interactive session on a compute
-node with an Intel Xeon Phi and load the module use following commands:Â
-
- $ qsub -I -q qmic -A NONE-0-0
-
- $ module load intel/13.5.192
-
-Please note that particular version of the Intel module is specified.
-This information is used later to specify the correct library paths.
-
-To produce a binary compatible with Intel Xeon Phi architecture user has
-to specify "-mmic" compiler flag. Two compilation examples are shown
-below. The first example shows how to compile OpenMP parallel code
-"vect-add.c" for host only:
-
- $ icc -xhost -no-offload -fopenmp vect-add.c -o vect-add-host
-
-To run this code on host, use:
-
- $ ./vect-add-host
-
-The second example shows how to compile the same code for Intel Xeon
-Phi:
-
- $ icc -mmic -fopenmp vect-add.c -o vect-add-mic
-
-### Execution of the Program in Native Mode on Intel Xeon Phi
-
-The user access to the Intel Xeon Phi is through the SSH. Since user
-home directories are mounted using NFS on the accelerator, users do not
-have to copy binary files or libraries between the host and accelerator.
-Â
-
-To connect to the accelerator run:
-
- $ ssh mic0
-
-If the code is sequential, it can be executed directly:
-
- mic0 $ ~/path_to_binary/vect-add-seq-mic
-
-If the code is parallelized using OpenMP a set of additional libraries
-is required for execution. To locate these libraries new path has to be
-added to the LD_LIBRARY_PATH environment variable prior to the
-execution:
-
- mic0 $ export LD_LIBRARY_PATH=/apps/intel/composer_xe_2013.5.192/compiler/lib/mic:$LD_LIBRARY_PATH
-
-Please note that the path exported in the previous example contains path
-to a specific compiler (here the version is 5.192). This version number
-has to match with the version number of the Intel compiler module that
-was used to compile the code on the host computer.
-
-For your information the list of libraries and their location required
-for execution of an OpenMP parallel code on Intel Xeon Phi is:
-
-/apps/intel/composer_xe_2013.5.192/compiler/lib/mic
-
-libiomp5.so
-libimf.so
-libsvml.so
-libirng.so
-libintlc.so.5
-
-Finally, to run the compiled code use:
-
- $ ~/path_to_binary/vect-add-mic
-
-OpenCL
--------------------
-
-OpenCL (Open Computing Language) is an open standard for
-general-purpose parallel programming for diverse mix of multi-core CPUs,
-GPU coprocessors, and other parallel processors. OpenCL provides a
-flexible execution model and uniform programming environment for
-software developers to write portable code for systems running on both
-the CPU and graphics processors or accelerators like the Intel® Xeon
-Phi.
-
-On Anselm OpenCL is installed only on compute nodes with MIC
-accelerator, therefore OpenCL code can be compiled only on these nodes.
-
- module load opencl-sdk opencl-rt
-
-Always load "opencl-sdk" (providing devel files like headers) and
-"opencl-rt" (providing dynamic library libOpenCL.so) modules to compile
-and link OpenCL code. Load "opencl-rt" for running your compiled code.
-
-There are two basic examples of OpenCL code in the following
-directory:
-
- /apps/intel/opencl-examples/
-
-First example "CapsBasic" detects OpenCL compatible hardware, here
-CPU and MIC, and prints basic information about the capabilities of
-it.Â
-
- /apps/intel/opencl-examples/CapsBasic/capsbasic
-
-To compile and run the example copy it to your home directory, get
-a PBS interactive session on of the nodes with MIC and run make for
-compilation. Make files are very basic and shows how the OpenCL code can
-be compiled on Anselm.
-
- $ cp /apps/intel/opencl-examples/CapsBasic/* .
- $ qsub -I -q qmic -A NONE-0-0
- $ make
-
-The compilation command for this example is:
-
- $ g++ capsbasic.cpp -lOpenCL -o capsbasic -I/apps/intel/opencl/include/
-
-After executing the complied binary file, following output should
-be displayed.
-
- ./capsbasic
-
- Number of available platforms: 1
- Platform names:
- Â Â Â [0] Intel(R) OpenCL [Selected]
- Number of devices available for each type:
- Â Â Â CL_DEVICE_TYPE_CPU: 1
- Â Â Â CL_DEVICE_TYPE_GPU: 0
- Â Â Â CL_DEVICE_TYPE_ACCELERATOR: 1
-
- ** Detailed information for each device ***
-
- CL_DEVICE_TYPE_CPU[0]
- Â Â Â CL_DEVICE_NAME:Â Â Â Â Â Â Â Intel(R) Xeon(R) CPU E5-2470 0 @ 2.30GHz
- Â Â Â CL_DEVICE_AVAILABLE: 1
-
- ...
-
- CL_DEVICE_TYPE_ACCELERATOR[0]
- Â Â Â CL_DEVICE_NAME: Intel(R) Many Integrated Core Acceleration Card
- Â Â Â CL_DEVICE_AVAILABLE: 1
-
- ...
-
-More information about this example can be found on Intel website:
-<http://software.intel.com/en-us/vcsource/samples/caps-basic/>
-
-The second example that can be found in
-"/apps/intel/opencl-examples" >directory is General Matrix
-Multiply. You can follow the the same procedure to download the example
-to your directory and compile it.Â
-
- $ cp -r /apps/intel/opencl-examples/* .
- $ qsub -I -q qmic -A NONE-0-0
- $ cd GEMM
- $ make
-
-The compilation command for this example is:
-
- $ g++ cmdoptions.cpp gemm.cpp ../common/basic.cpp ../common/cmdparser.cpp ../common/oclobject.cpp -I../common -lOpenCL -o gemm -I/apps/intel/opencl/include/
-
-To see the performance of Intel Xeon Phi performing the DGEMM run
-the example as follows:
-
- ./gemm -d 1
- Platforms (1):
- [0] Intel(R) OpenCL [Selected]
- Devices (2):
- [0] Intel(R) Xeon(R) CPU E5-2470 0 @ 2.30GHz
- [1] Intel(R) Many Integrated Core Acceleration Card [Selected]
- Build program options: "-DT=float -DTILE_SIZE_M=1 -DTILE_GROUP_M=16 -DTILE_SIZE_N=128 -DTILE_GROUP_N=1 -DTILE_SIZE_K=8"
- Running gemm_nn kernel with matrix size: 3968x3968
- Memory row stride to ensure necessary alignment: 15872 bytes
- Size of memory region for one matrix: 62980096 bytes
- Using alpha = 0.57599 and beta = 0.872412
- ...
- Host time: 0.292953 sec.
- Host perf: 426.635 GFLOPS
- Host time: 0.293334 sec.
- Host perf: 426.081 GFLOPS
- ...
-
-Please note: GNU compiler is used to compile the OpenCL codes for
-Intel MIC. You do not need to load Intel compiler module.
-
-MPIÂ
------------------
-
-### Environment setup and compilation
-
-Again an MPI code for Intel Xeon Phi has to be compiled on a compute
-node with accelerator and MPSS software stack installed. To get to a
-compute node with accelerator use:
-
- $ qsub -I -q qmic -A NONE-0-0
-
-The only supported implementation of MPI standard for Intel Xeon Phi is
-Intel MPI. To setup a fully functional development environment a
-combination of Intel compiler and Intel MPI has to be used. On a host
-load following modules before compilation:
-
- $ module load intel/13.5.192 impi/4.1.1.036
-
-To compile an MPI code for host use:
-
- $ mpiicc -xhost -o mpi-test mpi-test.c
-
-To compile the same code for Intel Xeon Phi architecture use:
-
- $ mpiicc -mmic -o mpi-test-mic mpi-test.c
-
-An example of basic MPI version of "hello-world" example in C language,
-that can be executed on both host and Xeon Phi is (can be directly copy
-and pasted to a .c file)
-
- #include <stdio.h>
- #include <mpi.h>
-
- int main (argc, argv)
- Â Â Â Â int argc;
- Â Â Â Â char *argv[];
- {
- Â int rank, size;
-
- Â int len;
- Â char node[MPI_MAX_PROCESSOR_NAME];
-
- Â MPI_Init (&argc, &argv);Â Â Â Â Â /* starts MPI */
- Â MPI_Comm_rank (MPI_COMM_WORLD, &rank);Â Â Â Â Â Â Â /* get current process id */
- Â MPI_Comm_size (MPI_COMM_WORLD, &size);Â Â Â Â Â Â Â /* get number of processes */
-
- Â MPI_Get_processor_name(node,&len);
-
- Â printf( "Hello world from process %d of %d on host %s n", rank, size, node );
- Â MPI_Finalize();
- Â return 0;
- }
-
-### MPI programming models
-
-Intel MPI for the Xeon Phi coprocessors offers different MPI
-programming models:
-
-Host-only model** - all MPI ranks reside on the host. The coprocessors
-can be used by using offload pragmas. (Using MPI calls inside offloaded
-code is not supported.)**
-
-Coprocessor-only model** - all MPI ranks reside only on the
-coprocessors.
-
-Symmetric model** - the MPI ranks reside on both the host and the
-coprocessor. Most general MPI case.
-
-###Host-only model
-
-In this case all environment variables are set by modules,
-so to execute the compiled MPI program on a single node, use:
-
- $ mpirun -np 4 ./mpi-test
-
-The output should be similar to:
-
- Hello world from process 1 of 4 on host cn207
- Hello world from process 3 of 4 on host cn207
- Hello world from process 2 of 4 on host cn207
- Hello world from process 0 of 4 on host cn207
-
-### Coprocessor-only model
-
-There are two ways how to execute an MPI code on a single
-coprocessor: 1.) lunch the program using "**mpirun**" from the
-coprocessor; or 2.) lunch the task using "**mpiexec.hydra**" from a
-host.
-
-Execution on coprocessor**Â
-
-Similarly to execution of OpenMP programs in native mode, since the
-environmental module are not supported on MIC, user has to setup paths
-to Intel MPI libraries and binaries manually. One time setup can be done
-by creating a "**.profile**" file in user's home directory. This file
-sets up the environment on the MIC automatically once user access to the
-accelerator through the SSH.
-
- $ vim ~/.profile
-
- PS1='[u@h W]$ '
- export PATH=/usr/bin:/usr/sbin:/bin:/sbin
-
- #OpenMP
- export LD_LIBRARY_PATH=/apps/intel/composer_xe_2013.5.192/compiler/lib/mic:$LD_LIBRARY_PATH
-
- #Intel MPI
- export LD_LIBRARY_PATH=/apps/intel/impi/4.1.1.036/mic/lib/:$LD_LIBRARY_PATH
- export PATH=/apps/intel/impi/4.1.1.036/mic/bin/:$PATH
-
-Please note:
-Â - this file sets up both environmental variable for both MPI and OpenMP
-libraries.
-Â - this file sets up the paths to a particular version of Intel MPI
-library and particular version of an Intel compiler. These versions have
-to match with loaded modules.
-
-To access a MIC accelerator located on a node that user is currently
-connected to, use:
-
- $ ssh mic0
-
-or in case you need specify a MIC accelerator on a particular node, use:
-
- $ ssh cn207-mic0
-
-To run the MPI code in parallel on multiple core of the accelerator,
-use:
-
- $ mpirun -np 4 ./mpi-test-mic
-
-The output should be similar to:
-
- Hello world from process 1 of 4 on host cn207-mic0
- Hello world from process 2 of 4 on host cn207-mic0
- Hello world from process 3 of 4 on host cn207-mic0
- Hello world from process 0 of 4 on host cn207-mic0
-
-**Execution on host**
-
-If the MPI program is launched from host instead of the coprocessor, the
-environmental variables are not set using the ".profile" file. Therefore
-user has to specify library paths from the command line when calling
-"mpiexec".
-
-First step is to tell mpiexec that the MPI should be executed on a local
-accelerator by setting up the environmental variable "I_MPI_MIC"
-
- $ export I_MPI_MIC=1
-
-Now the MPI program can be executed as:
-
- $ mpiexec.hydra -genv LD_LIBRARY_PATH /apps/intel/impi/4.1.1.036/mic/lib/ -host mic0 -n 4 ~/mpi-test-mic
-
-or using mpirun
-
- $ mpirun -genv LD_LIBRARY_PATH /apps/intel/impi/4.1.1.036/mic/lib/ -host mic0 -n 4 ~/mpi-test-mic
-
-Please note:
-Â - the full path to the binary has to specified (here:
-"**>~/mpi-test-mic**")
-Â - the LD_LIBRARY_PATH has to match with Intel MPI module used to
-compile the MPI code
-
-The output should be again similar to:
-
- Hello world from process 1 of 4 on host cn207-mic0
- Hello world from process 2 of 4 on host cn207-mic0
- Hello world from process 3 of 4 on host cn207-mic0
- Hello world from process 0 of 4 on host cn207-mic0
-
-Please note that the "mpiexec.hydra" requires a file
-"**>pmi_proxy**" from Intel MPI library to be copied to the
-MIC filesystem. If the file is missing please contact the system
-administrators. A simple test to see if the file is present is to
-execute:
-
- Â Â $ ssh mic0 ls /bin/pmi_proxy
- Â /bin/pmi_proxy
-
-**Execution on host - MPI processes distributed over multiple
-accelerators on multiple nodes**
-
-To get access to multiple nodes with MIC accelerator, user has to
-use PBS to allocate the resources. To start interactive session, that
-allocates 2 compute nodes = 2 MIC accelerators run qsub command with
-following parameters:
-
- $ qsub -I -q qmic -A NONE-0-0 -l select=2:ncpus=16
-
- $ module load intel/13.5.192 impi/4.1.1.036
-
-This command connects user through ssh to one of the nodes
-immediately. To see the other nodes that have been allocated use:
-
- $ cat $PBS_NODEFILE
-
-For example:
-
- cn204.bullx
- cn205.bullx
-
-This output means that the PBS allocated nodes cn204 and cn205,
-which means that user has direct access to "**cn204-mic0**" and
-"**cn-205-mic0**" accelerators.
-
-Please note: At this point user can connect to any of the
-allocated nodes or any of the allocated MIC accelerators using ssh:
-- to connect to the second node : ** $ ssh
-cn205**
-- to connect to the accelerator on the first node from the first
-node: **$ ssh cn204-mic0** or
- $ ssh mic0**
--** to connect to the accelerator on the second node from the first
-node: **$ ssh cn205-mic0**
-
-At this point we expect that correct modules are loaded and binary
-is compiled. For parallel execution the mpiexec.hydra is used.
-Again the first step is to tell mpiexec that the MPI can be executed on
-MIC accelerators by setting up the environmental variable "I_MPI_MIC"
-
- $ export I_MPI_MIC=1
-
-The launch the MPI program use:
-
- $ mpiexec.hydra -genv LD_LIBRARY_PATH /apps/intel/impi/4.1.1.036/mic/lib/
- -genv I_MPI_FABRICS_LIST tcp
- Â -genv I_MPI_FABRICS shm:tcp
- Â -genv I_MPI_TCP_NETMASK=10.1.0.0/16
- -host cn204-mic0 -n 4 ~/mpi-test-mic
- : -host cn205-mic0 -n 6 ~/mpi-test-mic
-
-or using mpirun:
-
- $ mpirun -genv LD_LIBRARY_PATH /apps/intel/impi/4.1.1.036/mic/lib/
- -genv I_MPI_FABRICS_LIST tcp
- Â -genv I_MPI_FABRICS shm:tcp
- Â -genv I_MPI_TCP_NETMASK=10.1.0.0/16
- -host cn204-mic0 -n 4 ~/mpi-test-mic
- : -host cn205-mic0 -n 6 ~/mpi-test-mic
-
-In this case four MPI processes are executed on accelerator cn204-mic
-and six processes are executed on accelerator cn205-mic0. The sample
-output (sorted after execution) is:
-
- Hello world from process 0 of 10 on host cn204-mic0
- Hello world from process 1 of 10 on host cn204-mic0
- Hello world from process 2 of 10 on host cn204-mic0
- Hello world from process 3 of 10 on host cn204-mic0
- Hello world from process 4 of 10 on host cn205-mic0
- Hello world from process 5 of 10 on host cn205-mic0
- Hello world from process 6 of 10 on host cn205-mic0
- Hello world from process 7 of 10 on host cn205-mic0
- Hello world from process 8 of 10 on host cn205-mic0
- Hello world from process 9 of 10 on host cn205-mic0
-
-The same way MPI program can be executed on multiple hosts:Â
-
- $ mpiexec.hydra -genv LD_LIBRARY_PATH /apps/intel/impi/4.1.1.036/mic/lib/
- -genv I_MPI_FABRICS_LIST tcp
- Â -genv I_MPI_FABRICS shm:tcp
- Â -genv I_MPI_TCP_NETMASK=10.1.0.0/16
- -host cn204 -n 4 ~/mpi-test
- : -host cn205 -n 6 ~/mpi-test
-
-###Symmetric model
-
-In a symmetric mode MPI programs are executed on both host
-computer(s) and MIC accelerator(s). Since MIC has a different
-architecture and requires different binary file produced by the Intel
-compiler two different files has to be compiled before MPI program is
-executed.
-
-In the previous section we have compiled two binary files, one for
-hosts "**mpi-test**" and one for MIC accelerators "**mpi-test-mic**".
-These two binaries can be executed at once using mpiexec.hydra:
-
- $ mpiexec.hydra
- -genv I_MPI_FABRICS_LIST tcp
- -genv I_MPI_FABRICS shm:tcp
- Â -genv I_MPI_TCP_NETMASK=10.1.0.0/16
- -genv LD_LIBRARY_PATH /apps/intel/impi/4.1.1.036/mic/lib/
- -host cn205 -n 2 ~/mpi-test
- : -host cn205-mic0 -n 2 ~/mpi-test-mic
-
-In this example the first two parameters (line 2 and 3) sets up required
-environment variables for execution. The third line specifies binary
-that is executed on host (here cn205) and the last line specifies the
-binary that is execute on the accelerator (here cn205-mic0).
-
-The output of the program is:
-
- Hello world from process 0 of 4 on host cn205
- Hello world from process 1 of 4 on host cn205
- Hello world from process 2 of 4 on host cn205-mic0
- Hello world from process 3 of 4 on host cn205-mic0
-
-The execution procedure can be simplified by using the mpirun
-command with the machine file a a parameter. Machine file contains list
-of all nodes and accelerators that should used to execute MPI processes.
-
-An example of a machine file that uses 2 >hosts (**cn205**
-and **cn206**) and 2 accelerators **(cn205-mic0** and **cn206-mic0**) to
-run 2 MPI processes on each of them:
-
- $ cat hosts_file_mix
- cn205:2
- cn205-mic0:2
- cn206:2
- cn206-mic0:2
-
-In addition if a naming convention is set in a way that the name
-of the binary for host is **"bin_name"**Â and the name of the binary
-for the accelerator is **"bin_name-mic"** then by setting up the
-environment variable **I_MPI_MIC_POSTFIX** to **"-mic"** user do not
-have to specify the names of booth binaries. In this case mpirun needs
-just the name of the host binary file (i.e. "mpi-test") and uses the
-suffix to get a name of the binary for accelerator (i..e.
-"mpi-test-mic").
-
- $ export I_MPI_MIC_POSTFIX=-mic
-
-Â >To run the MPI code using mpirun and the machine file
-"hosts_file_mix" use:
-
- $ mpirun
- -genv I_MPI_FABRICS shm:tcp
- -genv LD_LIBRARY_PATH /apps/intel/impi/4.1.1.036/mic/lib/
- -genv I_MPI_FABRICS_LIST tcp
- Â -genv I_MPI_FABRICS shm:tcp
- Â -genv I_MPI_TCP_NETMASK=10.1.0.0/16
- -machinefile hosts_file_mix
- ~/mpi-test
-
-A possible output of the MPI "hello-world" example executed on two
-hosts and two accelerators is:
-
- Hello world from process 0 of 8 on host cn204
- Hello world from process 1 of 8 on host cn204
- Hello world from process 2 of 8 on host cn204-mic0
- Hello world from process 3 of 8 on host cn204-mic0
- Hello world from process 4 of 8 on host cn205
- Hello world from process 5 of 8 on host cn205
- Hello world from process 6 of 8 on host cn205-mic0
- Hello world from process 7 of 8 on host cn205-mic0
-
-Please note: At this point the MPI communication between MIC
-accelerators on different nodes uses 1Gb Ethernet only.
-
-Using the PBS automatically generated node-files
-
-PBS also generates a set of node-files that can be used instead of
-manually creating a new one every time. Three node-files are genereated:
-
-**Host only node-file:**
-Â - /lscratch/${PBS_JOBID}/nodefile-cn
-MIC only node-file:
-Â - /lscratch/${PBS_JOBID}/nodefile-mic
-Host and MIC node-file:
-Â - /lscratch/${PBS_JOBID}/nodefile-mix
-
-Please note each host or accelerator is listed only per files. User has
-to specify how many jobs should be executed per node using "-n"
-parameter of the mpirun command.
-
-Optimization
-------------
-
-For more details about optimization techniques please read Intel
-document [Optimization and Performance Tuning for Intel® Xeon Phi™
-Coprocessors](http://software.intel.com/en-us/articles/optimization-and-performance-tuning-for-intel-xeon-phi-coprocessors-part-1-optimization "http://software.intel.com/en-us/articles/optimization-and-performance-tuning-for-intel-xeon-phi-coprocessors-part-1-optimization")
-
-Â
-
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--- a/converted/docs.it4i.cz/anselm-cluster-documentation/software/isv_licenses.md
+++ /dev/null
@@ -1,149 +0,0 @@
-ISV Licenses
-============
-
-A guide to managing Independent Software Vendor licences
-
-
-
-On Anselm cluster there are also installed commercial software
-applications, also known as ISV (Independent Software Vendor), which are
-subjects to licensing. The licenses are limited and their usage may be
-restricted only to some users or user groups.
-
-Currently Flex License Manager based licensing is supported on the
-cluster for products Ansys, Comsol and Matlab. More information about
-the applications can be found in the general
-[Software](../software.1.html) section.
-
-If an ISV application was purchased for educational (research) purposes
-and also for commercial purposes, then there are always two separate
-versions maintained and suffix "edu" is used in the name of the
-non-commercial version.
-
-Overview of the licenses usage
-------------------------------
-
-The overview is generated every minute and is accessible from web or
-command line interface.
-
-### Web interface
-
-For each license there is a table, which provides the information about
-the name, number of available (purchased/licensed), number of used and
-number of free license features
-
-<https://extranet.it4i.cz/anselm/licenses>
-
-### Text interface
-
-For each license there is a unique text file, which provides the
-information about the name, number of available (purchased/licensed),
-number of used and number of free license features. The text files are
-accessible from the Anselm command prompt.
-
- Product File with license state Note
- ------ |---|---|------------------------------------------- ---------------------
- ansys /apps/user/licenses/ansys_features_state.txt Commercial
- comsol /apps/user/licenses/comsol_features_state.txt Commercial
- comsol-edu /apps/user/licenses/comsol-edu_features_state.txt Non-commercial only
- matlab /apps/user/licenses/matlab_features_state.txt Commercial
- matlab-edu /apps/user/licenses/matlab-edu_features_state.txt Non-commercial only
-
-The file has a header which serves as a legend. All the info in the
-legend starts with a hash (#) so it can be easily filtered when parsing
-the file via a script.
-
-Example of the Commercial Matlab license state:
-
- $ cat /apps/user/licenses/matlab_features_state.txt
- # matlab
- # -------------------------------------------------
- # FEATURE TOTAL USED AVAIL
- # -------------------------------------------------
- MATLAB 1 1 0
- SIMULINK 1 0 1
- Curve_Fitting_Toolbox 1 0 1
- Signal_Blocks 1 0 1
- GADS_Toolbox 1 0 1
- Image_Toolbox 1 0 1
- Compiler 1 0 1
- Neural_Network_Toolbox 1 0 1
- Optimization_Toolbox 1 0 1
- Signal_Toolbox 1 0 1
- Statistics_Toolbox 1 0 1
-
-License tracking in PBS Pro scheduler and users usage
------------------------------------------------------
-
-Each feature of each license is accounted and checked by the scheduler
-of PBS Pro. If you ask for certain licences, the scheduler won't start
-the job until the asked licenses are free (available). This prevents to
-crash batch jobs, just because of
- unavailability of the
-needed licenses.
-
-The general format of the name is:
-
-feature__APP__FEATURE**
-
-Names of applications (APP):
-
-- ansys
-- comsol
-- comsol-edu
-- matlab
-- matlab-edu
-
-Â
-
-To get the FEATUREs of a license take a look into the corresponding
-state file ([see above](isv_licenses.html#Licence)), or
-use:
-
- |Application |List of provided features |
- | --- | --- |
- |ansys |<pre><code>$ grep -v "#" /apps/user/licenses/ansys_features_state.txt | cut -f1 -d' '</code></pre> |
- |comsol |<pre><code>$ grep -v "#" /apps/user/licenses/comsol_features_state.txt | cut -f1 -d' '</code></pre> |
- |comsol-edu |<pre><code>$ grep -v "#" /apps/user/licenses/comsol-edu_features_state.txt | cut -f1 -d' '</code></pre> |
- |matlab |<pre><code>$ grep -v "#" /apps/user/licenses/matlab_features_state.txt | cut -f1 -d' '</code></pre> |
- |matlab-edu |<pre><code>$ grep -v "#" /apps/user/licenses/matlab-edu_features_state.txt | cut -f1 -d' '</code></pre> |
-
-Â
-
-Example of PBS Pro resource name, based on APP and FEATURE name:
-
-<col width="33%" />
-<col width="33%" />
-<col width="33%" />
- |Application |Feature |PBS Pro resource name |
- | --- | --- |
- |ansys |acfd |feature__ansys__acfd |
- |ansys |aa_r |feature__ansys__aa_r |
- |comsol |COMSOL |feature__comsol__COMSOL |
- |comsol |HEATTRANSFER |feature__comsol__HEATTRANSFER |
- |comsol-edu |COMSOLBATCH |feature__comsol-edu__COMSOLBATCH |
- |comsol-edu |STRUCTURALMECHANICS |feature__comsol-edu__STRUCTURALMECHANICS |
- |matlab |MATLAB |feature__matlab__MATLAB |
- |matlab |Image_Toolbox |feature__matlab__Image_Toolbox |
- |matlab-edu |MATLAB_Distrib_Comp_Engine |feature__matlab-edu__MATLAB_Distrib_Comp_Engine |
- |matlab-edu |Image_Acquisition_Toolbox |feature__matlab-edu__Image_Acquisition_Toolbox\ |
-
-Be aware, that the resource names in PBS Pro are CASE SENSITIVE!**
-
-### Example of qsub statement
-
-Run an interactive PBS job with 1 Matlab EDU license, 1 Distributed
-Computing Toolbox and 32 Distributed Computing Engines (running on 32
-cores):
-
- $ qsub -I -q qprod -A PROJECT_ID -l select=2:ncpus=16 -l feature__matlab-edu__MATLAB=1 -l feature__matlab-edu__Distrib_Computing_Toolbox=1 -l feature__matlab-edu__MATLAB_Distrib_Comp_Engine=32
-
-The license is used and accounted only with the real usage of the
-product. So in this example, the general Matlab is used after Matlab is
-run vy the user and not at the time, when the shell of the interactive
-job is started. Also the Distributed Computing licenses are used at the
-time, when the user uses the distributed parallel computation in Matlab
-(e. g. issues pmode start, matlabpool, etc.).
-
-Â
-
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-Java
-====
-
-Java on ANSELM
-
-
-
-Java is available on Anselm cluster. Activate java by loading the java
-module
-
- $ module load java
-
-Note that the java module must be loaded on the compute nodes as well,
-in order to run java on compute nodes.
-
-Check for java version and path
-
- $ java -version
- $ which java
-
-With the module loaded, not only the runtime environment (JRE), but also
-the development environment (JDK) with the compiler is available.
-
- $ javac -version
- $ which javac
-
-Java applications may use MPI for interprocess communication, in
-conjunction with OpenMPI. Read more
-on <http://www.open-mpi.org/faq/?category=java>.
-This functionality is currently not supported on Anselm cluster. In case
-you require the java interface to MPI, please contact [Anselm
-support](https://support.it4i.cz/rt/).
-
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--- a/converted/docs.it4i.cz/anselm-cluster-documentation/software/kvirtualization.md
+++ /dev/null
@@ -1,484 +0,0 @@
-Virtualization
-==============
-
-Running virtual machines on compute nodes
-
-
-
-Introduction
-------------
-
-There are situations when Anselm's environment is not suitable for user
-needs.
-
-- Application requires different operating system (e.g Windows),
- application is not available for Linux
-- Application requires different versions of base system libraries and
- tools
-- Application requires specific setup (installation, configuration) of
- complex software stack
-- Application requires privileged access to operating system
-- ... and combinations of above cases
-
-Â We offer solution for these cases - **virtualization**. Anselm's
-environment gives the possibility to run virtual machines on compute
-nodes. Users can create their own images of operating system with
-specific software stack and run instances of these images as virtual
-machines on compute nodes. Run of virtual machines is provided by
-standard mechanism of [Resource Allocation and Job
-Execution](../../resource-allocation-and-job-execution/introduction.html).
-
-Solution is based on QEMU-KVM software stack and provides
-hardware-assisted x86 virtualization.
-
-Limitations
------------
-
-Anselm's infrastructure was not designed for virtualization. Anselm's
-environment is not intended primary for virtualization, compute nodes,
-storages and all infrastructure of Anselm is intended and optimized for
-running HPC jobs, this implies suboptimal configuration of
-virtualization and limitations.
-
-Anselm's virtualization does not provide performance and all features of
-native environment. There is significant performance hit (degradation)
-in I/O performance (storage, network). Anselm's virtualization is not
-suitable for I/O (disk, network) intensive workloads.
-
-Virtualization has also some drawbacks, it is not so easy to setup
-efficient solution.
-
-Solution described in chapter
-[HOWTO](virtualization.html#howto)
- is suitable for single node tasks, does not
-introduce virtual machine clustering.
-
-Please consider virtualization as last resort solution for your needs.
-
-Please consult use of virtualization with IT4Innovation's support.
-
-For running Windows application (when source code and Linux native
-application are not available) consider use of Wine, Windows
-compatibility layer. Many Windows applications can be run using Wine
-with less effort and better performance than when using virtualization.
-
-Licensing
----------
-
-IT4Innovations does not provide any licenses for operating systems and
-software of virtual machines. Users are ( >
-in accordance with [Acceptable use policy
-document](http://www.it4i.cz/acceptable-use-policy.pdf))
-fully responsible for licensing all software running in virtual machines
-on Anselm. Be aware of complex conditions of licensing software in
-virtual environments.
-
-Users are responsible for licensing OS e.g. MS Windows and all software
-running in their virtual machines.
-
-Â HOWTO
-----------
-
-### Virtual Machine Job Workflow
-
-We propose this job workflow:
-
-Workflow](virtualization-job-workflow "Virtualization Job Workflow")
-
-Our recommended solution is that job script creates distinct shared job
-directory, which makes a central point for data exchange between
-Anselm's environment, compute node (host) (e.g HOME, SCRATCH, local
-scratch and other local or cluster filesystems) and virtual machine
-(guest). Job script links or copies input data and instructions what to
-do (run script) for virtual machine to job directory and virtual machine
-process input data according instructions in job directory and store
-output back to job directory. We recommend, that virtual machine is
-running in so called [snapshot
-mode](virtualization.html#snapshot-mode), image is
-immutable - image does not change, so one image can be used for many
-concurrent jobs.
-
-### Procedure
-
-1. Prepare image of your virtual machine
-2. Optimize image of your virtual machine for Anselm's virtualization
-3. Modify your image for running jobs
-4. Create job script for executing virtual machine
-5. Run jobs
-
-### Prepare image of your virtual machine
-
-You can either use your existing image or create new image from scratch.
-
-QEMU currently supports these image types or formats:
-
-- rawÂ
-- cloopÂ
-- cowÂ
-- qcowÂ
-- qcow2Â
-- vmdk - VMware 3 & 4, or 6 image format, for exchanging images with
- that product
-- vdi - VirtualBox 1.1 compatible image format, for exchanging images
- with VirtualBox.
-
-You can convert your existing image using qemu-img convert command.
-Supported formats of this command are: blkdebug blkverify bochs cloop
-cow dmg file ftp ftps host_cdrom host_device host_floppy http https
-nbd parallels qcow qcow2 qed raw sheepdog tftp vdi vhdx vmdk vpc vvfat.
-
-We recommend using advanced QEMU native image format qcow2.
-
-[More about QEMU
-Images](http://en.wikibooks.org/wiki/QEMU/Images)
-
-### Optimize image of your virtual machine
-
-Use virtio devices (for disk/drive and network adapter) and install
-virtio drivers (paravirtualized drivers) into virtual machine. There is
-significant performance gain when using virtio drivers. For more
-information see [Virtio
-Linux](http://www.linux-kvm.org/page/Virtio) and [Virtio
-Windows](http://www.linux-kvm.org/page/WindowsGuestDrivers/Download_Drivers).
-
-Disable all
-unnecessary services
-and tasks. Restrict all unnecessary operating system operations.
-
-Remove all
-unnecessary software and
-files.
-
-
-Remove all paging
-space, swap files, partitions, etc.
-
-Shrink your image. (It is recommended to zero all free space and
-reconvert image using qemu-img.)
-
-### Modify your image for running jobs
-
-Your image should run some kind of operating system startup script.
-Startup script should run application and when application exits run
-shutdown or quit virtual machine.
-
-We recommend, that startup script
-
-maps Job Directory from host (from compute node)
-runs script (we call it "run script") from Job Directory and waits for
-application's exit
-- for management purposes if run script does not exist wait for some
- time period (few minutes)
-
-shutdowns/quits OS
-For Windows operating systems we suggest using Local Group Policy
-Startup script, for Linux operating systems rc.local, runlevel init
-script or similar service.
-
-Example startup script for Windows virtual machine:
-
- @echo off
- set LOG=c:startup.log
- set MAPDRIVE=z:
- set SCRIPT=%MAPDRIVE%run.bat
- set TIMEOUT=300
-
- echo %DATE% %TIME% Running startup script>%LOG%
-
- rem Mount share
- echo %DATE% %TIME% Mounting shared drive>%LOG%
- net use z: 10.0.2.4qemu >%LOG% 2>&1
- dir z: >%LOG% 2>&1
- echo. >%LOG%
-
- if exist %MAPDRIVE% (
- Â echo %DATE% %TIME% The drive "%MAPDRIVE%" exists>%LOG%
-
- Â if exist %SCRIPT% (
- Â Â Â echo %DATE% %TIME% The script file "%SCRIPT%"exists>%LOG%
- Â Â Â echo %DATE% %TIME% Running script %SCRIPT%>%LOG%
- Â Â Â set TIMEOUT=0
- Â Â Â call %SCRIPT%
- Â ) else (
- Â Â Â echo %DATE% %TIME% The script file "%SCRIPT%"does not exist>%LOG%
- Â )
-
- ) else (
- Â echo %DATE% %TIME% The drive "%MAPDRIVE%" does not exist>%LOG%
- )
- echo. >%LOG%
-
- timeout /T %TIMEOUT%
-
- echo %DATE% %TIME% Shut down>%LOG%
- shutdown /s /t 0
-
-Example startup script maps shared job script as drive z: and looks for
-run script called run.bat. If run script is found it is run else wait
-for 5 minutes, then shutdown virtual machine.
-
-### Create job script for executing virtual machine
-
-Create job script according recommended
-
-[Virtual Machine Job
-Workflow](virtualization.html#virtual-machine-job-workflow).
-
-Example job for Windows virtual machine:
-
- #/bin/sh
-
- JOB_DIR=/scratch/$USER/win/${PBS_JOBID}
-
- #Virtual machine settings
- VM_IMAGE=~/work/img/win.img
- VM_MEMORY=49152
- VM_SMP=16
-
- # Prepare job dir
- mkdir -p ${JOB_DIR} && cd ${JOB_DIR} || exit 1
- ln -s ~/work/win .
- ln -s /scratch/$USER/data .
- ln -s ~/work/win/script/run/run-appl.bat run.bat
-
- # Run virtual machine
- export TMPDIR=/lscratch/${PBS_JOBID}
- module add qemu
- qemu-system-x86_64
- Â -enable-kvm
- Â -cpu host
- Â -smp ${VM_SMP}
- Â -m ${VM_MEMORY}
- Â -vga std
- Â -localtime
- Â -usb -usbdevice tablet
- Â -device virtio-net-pci,netdev=net0
- Â -netdev user,id=net0,smb=${JOB_DIR},hostfwd=tcp::3389-:3389
- Â -drive file=${VM_IMAGE},media=disk,if=virtio
- Â -snapshot
- Â -nographic
-
-Job script links application data (win), input data (data) and run
-script (run.bat) into job directory and runs virtual machine.
-
-Example run script (run.bat) for Windows virtual machine:
-
- z:
- cd winappl
- call application.bat z:data z:output
-
-Run script runs application from shared job directory (mapped as drive
-z:), process input data (z:data) from job directory and store output
-to job directory (z:output).
-
-### Run jobs
-
-Run jobs as usual, see [Resource Allocation and Job
-Execution](../../resource-allocation-and-job-execution/introduction.html).
-Use only full node allocation for virtualization jobs.
-
-### Running Virtual Machines
-
-Virtualization is enabled only on compute nodes, virtualization does not
-work on login nodes.
-
-Load QEMU environment module:
-
- $ module add qemu
-
-Get help
-
- $ man qemu
-
-Run virtual machine (simple)
-
- $ qemu-system-x86_64 -hda linux.img -enable-kvm -cpu host -smp 16 -m 32768 -vga std -vnc :0
-
- $ qemu-system-x86_64 -hda win.img -enable-kvm -cpu host -smp 16 -m 32768 -vga std -localtime -usb -usbdevice tablet -vnc :0
-
-You can access virtual machine by VNC viewer (option -vnc) connecting to
-IP address of compute node. For VNC you must use [VPN
-network](../../accessing-the-cluster/vpn-access.html).
-
-Install virtual machine from iso file
-
- $ qemu-system-x86_64 -hda linux.img -enable-kvm -cpu host -smp 16 -m 32768 -vga std -cdrom linux-install.iso -boot d -vnc :0
-
- $ qemu-system-x86_64 -hda win.img -enable-kvm -cpu host -smp 16 -m 32768 -vga std -localtime -usb -usbdevice tablet -cdrom win-install.iso -boot d -vnc :0
-
-Run virtual machine using optimized devices, user network backend with
-sharing and port forwarding, in snapshot mode
-
- $ qemu-system-x86_64 -drive file=linux.img,media=disk,if=virtio -enable-kvm -cpu host -smp 16 -m 32768 -vga std -device virtio-net-pci,netdev=net0 -netdev user,id=net0,smb=/scratch/$USER/tmp,hostfwd=tcp::2222-:22 -vnc :0 -snapshot
-
- $ qemu-system-x86_64 -drive file=win.img,media=disk,if=virtio -enable-kvm -cpu host -smp 16 -m 32768 -vga std -localtime -usb -usbdevice tablet -device virtio-net-pci,netdev=net0 -netdev user,id=net0,smb=/scratch/$USER/tmp,hostfwd=tcp::3389-:3389 -vnc :0 -snapshot
-
-Thanks to port forwarding you can access virtual machine via SSH (Linux)
-or RDP (Windows) connecting to IP address of compute node (and port 2222
-for SSH). You must use [VPN
-network](../../accessing-the-cluster/vpn-access.html).
-
-Keep in mind, that if you use virtio devices, you must have virtio
-drivers installed on your virtual machine.
-
-### Networking and data sharing
-
-For networking virtual machine we suggest to use (default) user network
-backend (sometimes called slirp). This network backend NATs virtual
-machines and provides useful services for virtual machines as DHCP, DNS,
-SMB sharing, port forwarding.
-
-In default configuration IP network 10.0.2.0/24 is used, host has IP
-address 10.0.2.2, DNS server 10.0.2.3, SMB server 10.0.2.4 and virtual
-machines obtain address from range 10.0.2.15-10.0.2.31. Virtual machines
-have access to Anselm's network via NAT on compute node (host).
-
-Simple network setup
-
- $ qemu-system-x86_64 ... -net nic -net user
-
-(It is default when no -net options are given.)
-
-Simple network setup with sharing and port forwarding (obsolete but
-simpler syntax, lower performance)
-
- $ qemu-system-x86_64 ... -net nic -net user,smb=/scratch/$USER/tmp,hostfwd=tcp::3389-:3389
-
-Optimized network setup with sharing and port forwarding
-
- $ qemu-system-x86_64 ... -device virtio-net-pci,netdev=net0 -netdev user,id=net0,smb=/scratch/$USER/tmp,hostfwd=tcp::2222-:22
-
-### Advanced networking
-
-Internet access**
-
-Sometime your virtual machine needs access to internet (install
-software, updates, software activation, etc). We suggest solution using
-Virtual Distributed Ethernet (VDE) enabled QEMU with SLIRP running on
-login node tunnelled to compute node. Be aware, this setup has very low
-performance, the worst performance of all described solutions.
-
-Load VDE enabled QEMU environment module (unload standard QEMU module
-first if necessary).
-
- $ module add qemu/2.1.2-vde2
-
-Create virtual network switch.
-
- $ vde_switch -sock /tmp/sw0 -mgmt /tmp/sw0.mgmt -daemon
-
-Run SLIRP daemon over SSH tunnel on login node and connect it to virtual
-network switch.
-
- $ dpipe vde_plug /tmp/sw0 = ssh login1 $VDE2_DIR/bin/slirpvde -s - --dhcp &
-
-Run qemu using vde network backend, connect to created virtual switch.
-
-Basic setup (obsolete syntax)
-
- $ qemu-system-x86_64 ... -net nic -net vde,sock=/tmp/sw0
-
-Setup using virtio device (obsolete syntax)
-
- $ qemu-system-x86_64 ... -net nic,model=virtio -net vde,sock=/tmp/sw0
-
-Optimized setup
-
- $ qemu-system-x86_64 ... -device virtio-net-pci,netdev=net0 -netdev vde,id=net0,sock=/tmp/sw0
-
-TAP interconnect**
-
-Both user and vde network backend have low performance. For fast
-interconnect (10Gbps and more) of compute node (host) and virtual
-machine (guest) we suggest using Linux kernel TAP device.
-
-Cluster Anselm provides TAP device tap0 for your job. TAP interconnect
-does not provide any services (like NAT, DHCP, DNS, SMB, etc.) just raw
-networking, so you should provide your services if you need them.
-
-Run qemu with TAP network backend:
-
- $ qemu-system-x86_64 ... -device virtio-net-pci,netdev=net1
- -netdev tap,id=net1,ifname=tap0,script=no,downscript=no
-
-Interface tap0 has IP address 192.168.1.1 and network mask 255.255.255.0
-(/24). In virtual machine use IP address from range
-192.168.1.2-192.168.1.254. For your convenience some ports on tap0
-interface are redirected to higher numbered ports, so you as
-non-privileged user can provide services on these ports.
-
-Redirected ports:
-
-- DNS udp/53->udp/3053, tcp/53->tcp3053
-- DHCP udp/67->udp3067
-- SMB tcp/139->tcp3139, tcp/445->tcp3445).
-
-You can configure IP address of virtual machine statically or
-dynamically. For dynamic addressing provide your DHCP server on port
-3067 of tap0 interface, you can also provide your DNS server on port
-3053 of tap0 interface for example:
-
- $ dnsmasq --interface tap0 --bind-interfaces -p 3053 --dhcp-alternate-port=3067,68 --dhcp-range=192.168.1.15,192.168.1.32 --dhcp-leasefile=/tmp/dhcp.leasefile
-
-You can also provide your SMB services (on ports 3139, 3445) to obtain
-high performance data sharing.
-
-Example smb.conf (not optimized)
-
- [global]
- socket address=192.168.1.1
- smb ports = 3445 3139
-
- private dir=/tmp/qemu-smb
- pid directory=/tmp/qemu-smb
- lock directory=/tmp/qemu-smb
- state directory=/tmp/qemu-smb
- ncalrpc dir=/tmp/qemu-smb/ncalrpc
- log file=/tmp/qemu-smb/log.smbd
- smb passwd file=/tmp/qemu-smb/smbpasswd
- security = user
- map to guest = Bad User
- unix extensions = no
- load printers = no
- printing = bsd
- printcap name = /dev/null
- disable spoolss = yes
- log level = 1
- guest account = USER
- [qemu]
- path=/scratch/USER/tmp
- read only=no
- guest ok=yes
- writable=yes
- follow symlinks=yes
- wide links=yes
- force user=USER
-
-(Replace USER with your login name.)
-
-Run SMB services
-
- smbd -s /tmp/qemu-smb/smb.conf
-
-Â
-
-Virtual machine can of course have more than one network interface
-controller, virtual machine can use more than one network backend. So,
-you can combine for example use network backend and TAP interconnect.
-
-### Snapshot mode
-
-In snapshot mode image is not written, changes are written to temporary
-file (and discarded after virtual machine exits). **It is strongly
-recommended mode for running your jobs.** Set TMPDIR environment
-variable to local scratch directory for placement temporary files.
-
- $ export TMPDIR=/lscratch/${PBS_JOBID}
- $ qemu-system-x86_64 ... -snapshot
-
-### Windows guests
-
-For Windows guests we recommend these options, life will be easier:
-
- $ qemu-system-x86_64 ... -localtime -usb -usbdevice tablet
-
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-Running OpenMPI
-===============
-
-
-
-OpenMPI program execution
--------------------------
-
-The OpenMPI programs may be executed only via the PBS Workload manager,
-by entering an appropriate queue. On Anselm, the **bullxmpi-1.2.4.1**
-and **OpenMPI 1.6.5** are OpenMPI based MPI implementations.
-
-### Basic usage
-
-Use the mpiexec to run the OpenMPI code.
-
-Example:
-
- $ qsub -q qexp -l select=4:ncpus=16 -I
- qsub: waiting for job 15210.srv11 to start
- qsub: job 15210.srv11 ready
-
- $ pwd
- /home/username
-
- $ module load openmpi
- $ mpiexec -pernode ./helloworld_mpi.x
- Hello world! from rank 0 of 4 on host cn17
- Hello world! from rank 1 of 4 on host cn108
- Hello world! from rank 2 of 4 on host cn109
- Hello world! from rank 3 of 4 on host cn110
-
-Please be aware, that in this example, the directive **-pernode** is
-used to run only **one task per node**, which is normally an unwanted
-behaviour (unless you want to run hybrid code with just one MPI and 16
-OpenMP tasks per node). In normal MPI programs **omit the -pernode
-directive** to run up to 16 MPI tasks per each node.
-
-In this example, we allocate 4 nodes via the express queue
-interactively. We set up the openmpi environment and interactively run
-the helloworld_mpi.x program.
-Note that the executable
-helloworld_mpi.x must be available within the
-same path on all nodes. This is automatically fulfilled on the /home and
-/scratch filesystem.
-
-You need to preload the executable, if running on the local scratch
-/lscratch filesystem
-
- $ pwd
- /lscratch/15210.srv11
-
- $ mpiexec -pernode --preload-binary ./helloworld_mpi.x
- Hello world! from rank 0 of 4 on host cn17
- Hello world! from rank 1 of 4 on host cn108
- Hello world! from rank 2 of 4 on host cn109
- Hello world! from rank 3 of 4 on host cn110
-
-In this example, we assume the executable
-helloworld_mpi.x is present on compute node
-cn17 on local scratch. We call the mpiexec whith the
---preload-binary** argument (valid for openmpi). The mpiexec will copy
-the executable from cn17 to the
-/lscratch/15210.srv11 directory on cn108, cn109
-and cn110 and execute the program.
-
-MPI process mapping may be controlled by PBS parameters.
-
-The mpiprocs and ompthreads parameters allow for selection of number of
-running MPI processes per node as well as number of OpenMP threads per
-MPI process.
-
-### One MPI process per node
-
-Follow this example to run one MPI process per node, 16 threads per
-process.Â
-
- $ qsub -q qexp -l select=4:ncpus=16:mpiprocs=1:ompthreads=16 -I
-
- $ module load openmpi
-
- $ mpiexec --bind-to-none ./helloworld_mpi.x
-
-In this example, we demonstrate recommended way to run an MPI
-application, using 1 MPI processes per node and 16 threads per socket,
-on 4 nodes.
-
-### Two MPI processes per node
-
-Follow this example to run two MPI processes per node, 8 threads per
-process. Note the options to mpiexec.
-
- $ qsub -q qexp -l select=4:ncpus=16:mpiprocs=2:ompthreads=8 -I
-
- $ module load openmpi
-
- $ mpiexec -bysocket -bind-to-socket ./helloworld_mpi.x
-
-In this example, we demonstrate recommended way to run an MPI
-application, using 2 MPI processes per node and 8 threads per socket,
-each process and its threads bound to a separate processor socket of the
-node, on 4 nodes
-
-### 16 MPI processes per node
-
-Follow this example to run 16 MPI processes per node, 1 thread per
-process. Note the options to mpiexec.
-
- $ qsub -q qexp -l select=4:ncpus=16:mpiprocs=16:ompthreads=1 -I
-
- $ module load openmpi
-
- $ mpiexec -bycore -bind-to-core ./helloworld_mpi.x
-
-In this example, we demonstrate recommended way to run an MPI
-application, using 16 MPI processes per node, single threaded. Each
-process is bound to separate processor core, on 4 nodes.
-
-### OpenMP thread affinity
-
-Important! Bind every OpenMP thread to a core!
-
-In the previous two examples with one or two MPI processes per node, the
-operating system might still migrate OpenMP threads between cores. You
-might want to avoid this by setting these environment variable for GCC
-OpenMP:
-
- $ export GOMP_CPU_AFFINITY="0-15"
-
-or this one for Intel OpenMP:
-
- $ export KMP_AFFINITY=granularity=fine,compact,1,0
-
-As of OpenMP 4.0 (supported by GCC 4.9 and later and Intel 14.0 and
-later) the following variables may be used for Intel or GCC:
-
- $ export OMP_PROC_BIND=true
- $ export OMP_PLACES=coresÂ
-
-OpenMPI Process Mapping and Binding
-------------------------------------------------
-
-The mpiexec allows for precise selection of how the MPI processes will
-be mapped to the computational nodes and how these processes will bind
-to particular processor sockets and cores.
-
-MPI process mapping may be specified by a hostfile or rankfile input to
-the mpiexec program. Altough all implementations of MPI provide means
-for process mapping and binding, following examples are valid for the
-openmpi only.
-
-### Hostfile
-
-Example hostfile
-
- cn110.bullx
- cn109.bullx
- cn108.bullx
- cn17.bullx
-
-Use the hostfile to control process placement
-
- $ mpiexec -hostfile hostfile ./helloworld_mpi.x
- Hello world! from rank 0 of 4 on host cn110
- Hello world! from rank 1 of 4 on host cn109
- Hello world! from rank 2 of 4 on host cn108
- Hello world! from rank 3 of 4 on host cn17
-
-In this example, we see that ranks have been mapped on nodes according
-to the order in which nodes show in the hostfile
-
-### Rankfile
-
-Exact control of MPI process placement and resource binding is provided
-by specifying a rankfile
-
-Appropriate binding may boost performance of your application.
-
-Example rankfile
-
- rank 0=cn110.bullx slot=1:0,1
- rank 1=cn109.bullx slot=0:*
- rank 2=cn108.bullx slot=1:1-2
- rank 3=cn17.bullx slot=0:1,1:0-2
- rank 4=cn109.bullx slot=0:*,1:*
-
-This rankfile assumes 5 ranks will be running on 4 nodes and provides
-exact mapping and binding of the processes to the processor sockets and
-cores
-
-Explanation:
-rank 0 will be bounded to cn110, socket1 core0 and core1
-rank 1 will be bounded to cn109, socket0, all cores
-rank 2 will be bounded to cn108, socket1, core1 and core2
-rank 3 will be bounded to cn17, socket0 core1, socket1 core0, core1,
-core2
-rank 4 will be bounded to cn109, all cores on both sockets
-
- $ mpiexec -n 5 -rf rankfile --report-bindings ./helloworld_mpi.x
- [cn17:11180] MCW rank 3 bound to socket 0[core 1] socket 1[core 0-2]: [. B . . . . . .][B B B . . . . .] (slot list 0:1,1:0-2)
- [cn110:09928] MCW rank 0 bound to socket 1[core 0-1]: [. . . . . . . .][B B . . . . . .] (slot list 1:0,1)
- [cn109:10395] MCW rank 1 bound to socket 0[core 0-7]: [B B B B B B B B][. . . . . . . .] (slot list 0:*)
- [cn108:10406] MCW rank 2 bound to socket 1[core 1-2]: [. . . . . . . .][. B B . . . . .] (slot list 1:1-2)
- [cn109:10406] MCW rank 4 bound to socket 0[core 0-7] socket 1[core 0-7]: [B B B B B B B B][B B B B B B B B] (slot list 0:*,1:*)
- Hello world! from rank 3 of 5 on host cn17
- Hello world! from rank 1 of 5 on host cn109
- Hello world! from rank 0 of 5 on host cn110
- Hello world! from rank 4 of 5 on host cn109
- Hello world! from rank 2 of 5 on host cn108
-
-In this example we run 5 MPI processes (5 ranks) on four nodes. The
-rankfile defines how the processes will be mapped on the nodes, sockets
-and cores. The **--report-bindings** option was used to print out the
-actual process location and bindings. Note that ranks 1 and 4 run on the
-same node and their core binding overlaps.
-
-It is users responsibility to provide correct number of ranks, sockets
-and cores.
-
-### Bindings verification
-
-In all cases, binding and threading may be verified by executing for
-example:
-
- $ mpiexec -bysocket -bind-to-socket --report-bindings echo
- $ mpiexec -bysocket -bind-to-socket numactl --show
- $ mpiexec -bysocket -bind-to-socket echo $OMP_NUM_THREADS
-
-Changes in OpenMPI 1.8
-----------------------
-
-Some options have changed in OpenMPI version 1.8.
-
- |version 1.6.5 |version 1.8.1 |
- | --- | --- |
- |--bind-to-none |--bind-to none |
- |--bind-to-core |--bind-to core |
- |--bind-to-socket |--bind-to socket |
- |-bysocket |--map-by socket |
- |-bycore |--map-by core |
- |-pernode |--map-by ppr:1:node\ |
-
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-MPI
-===
-
-
-
-Setting up MPI Environment
---------------------------
-
-The Anselm cluster provides several implementations of the MPI library:
-
- |MPI Library |Thread support |
- | --- | --- |
- |The highly optimized and stable <strong>bullxmpi 1.2.4.1</strong>\ |<strong></strong>Partial thread support up to MPI_THREAD_SERIALIZED |
- |The <strong>Intel MPI 4.1</strong> |Full thread support up to MPI_THREAD_MULTIPLE |
- |The <a href="http://www.open-mpi.org/" <strong>OpenMPI 1.6.5</strong></a> |Full thread support up to MPI_THREAD_MULTIPLE, BLCR c/r support |
- |The OpenMPI 1.8.1 |Full thread support up to MPI_THREAD_MULTIPLE, MPI-3.0 support |
- |The <strong><strong>mpich2 1.9</strong></strong> |Full thread support up to <strong></strong> MPI_THREAD_MULTIPLE, BLCR c/r support |
-
-MPI libraries are activated via the environment modules.
-
-Look up section modulefiles/mpi in module avail
-
- $ module avail
- ------------------------- /opt/modules/modulefiles/mpi -------------------------
- bullxmpi/bullxmpi-1.2.4.1 mvapich2/1.9-icc
- impi/4.0.3.008 openmpi/1.6.5-gcc(default)
- impi/4.1.0.024 openmpi/1.6.5-gcc46
- impi/4.1.0.030 openmpi/1.6.5-icc
- impi/4.1.1.036(default) openmpi/1.8.1-gcc
- openmpi/1.8.1-gcc46
- mvapich2/1.9-gcc(default) openmpi/1.8.1-gcc49
- mvapich2/1.9-gcc46 openmpi/1.8.1-icc
-
-There are default compilers associated with any particular MPI
-implementation. The defaults may be changed, the MPI libraries may be
-used in conjunction with any compiler.
-The defaults are selected via the modules in following way
-
- Module MPI Compiler suite
- -------- |---|---|-------- --------------------------------------------------------------------------------
- PrgEnv-gnu bullxmpi-1.2.4.1 bullx GNU 4.4.6
- PrgEnv-intel Intel MPI 4.1.1 Intel 13.1.1
- bullxmpi bullxmpi-1.2.4.1 none, select via module
- impi Intel MPI 4.1.1 none, select via module
- openmpi OpenMPI 1.6.5 GNU compilers 4.8.1, GNU compilers 4.4.6, Intel Compilers
- openmpi OpenMPI 1.8.1 GNU compilers 4.8.1, GNU compilers 4.4.6, GNU compilers 4.9.0, Intel Compilers
- mvapich2 MPICH2 1.9 GNU compilers 4.8.1, GNU compilers 4.4.6, Intel Compilers
-
-Examples:
-
- $ module load openmpi
-
-In this example, we activate the latest openmpi with latest GNU
-compilers
-
-To use openmpi with the intel compiler suite, use
-
- $ module load intel
- $ module load openmpi/1.6.5-icc
-
-In this example, the openmpi 1.6.5 using intel compilers is activated
-
-Compiling MPI Programs
-----------------------
-
-After setting up your MPI environment, compile your program using one of
-the mpi wrappers
-
- $ mpicc -v
- $ mpif77 -v
- $ mpif90 -v
-
-Example program:
-
- // helloworld_mpi.c
- #include <stdio.h>
-
- #include<mpi.h>
-
- int main(int argc, char **argv) {
-
- int len;
- int rank, size;
- char node[MPI_MAX_PROCESSOR_NAME];
-
- // Initiate MPI
- MPI_Init(&argc, &argv);
- MPI_Comm_rank(MPI_COMM_WORLD,&rank);
- MPI_Comm_size(MPI_COMM_WORLD,&size);
-
- // Get hostame and print
- MPI_Get_processor_name(node,&len);
- printf("Hello world! from rank %d of %d on host %sn",rank,size,node);
-
- // Finalize and exit
- MPI_Finalize();
-
- return 0;
- }
-
-Compile the above example with
-
- $ mpicc helloworld_mpi.c -o helloworld_mpi.x
-
-Running MPI Programs
---------------------
-
-The MPI program executable must be compatible with the loaded MPI
-module.
-Always compile and execute using the very same MPI module.
-
-It is strongly discouraged to mix mpi implementations. Linking an
-application with one MPI implementation and running mpirun/mpiexec form
-other implementation may result in unexpected errors.
-
-The MPI program executable must be available within the same path on all
-nodes. This is automatically fulfilled on the /home and /scratch
-filesystem. You need to preload the executable, if running on the local
-scratch /lscratch filesystem.
-
-### Ways to run MPI programs
-
-Optimal way to run an MPI program depends on its memory requirements,
-memory access pattern and communication pattern.
-
-Consider these ways to run an MPI program:
-1. One MPI process per node, 16 threads per process
-2. Two MPI processes per node, 8 threads per process
-3. 16 MPI processes per node, 1 thread per process.
-
-One MPI** process per node, using 16 threads, is most useful for
-memory demanding applications, that make good use of processor cache
-memory and are not memory bound. This is also a preferred way for
-communication intensive applications as one process per node enjoys full
-bandwidth access to the network interface.Â
-
-Two MPI** processes per node, using 8 threads each, bound to processor
-socket is most useful for memory bandwidth bound applications such as
-BLAS1 or FFT, with scalable memory demand. However, note that the two
-processes will share access to the network interface. The 8 threads and
-socket binding should ensure maximum memory access bandwidth and
-minimize communication, migration and numa effect overheads.
-
-Important! Bind every OpenMP thread to a core!
-
-In the previous two cases with one or two MPI processes per node, the
-operating system might still migrate OpenMP threads between cores. You
-want to avoid this by setting the KMP_AFFINITY or GOMP_CPU_AFFINITY
-environment variables.
-
-16 MPI** processes per node, using 1 thread each bound to processor
-core is most suitable for highly scalable applications with low
-communication demand.
-
-### Running OpenMPI
-
-The **bullxmpi-1.2.4.1** and [**OpenMPI
-1.6.5**](http://www.open-mpi.org/) are both based on
-OpenMPI. Read more on [how to run
-OpenMPI](Running_OpenMPI.html) based MPI.
-
-### Running MPICH2
-
-The **Intel MPI** and **mpich2 1.9** are MPICH2 based implementations.
-Read more on [how to run MPICH2](running-mpich2.html)
-based MPI.
-
-The Intel MPI may run on the Intel Xeon Phi accelerators as well. Read
-more on [how to run Intel MPI on
-accelerators](../intel-xeon-phi.html).
-
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-MPI4Py (MPI for Python)
-=======================
-
-OpenMPI interface to Python
-
-
-
-Introduction
-------------
-
-MPI for Python provides bindings of the Message Passing Interface (MPI)
-standard for the Python programming language, allowing any Python
-program to exploit multiple processors.
-
-This package is constructed on top of the MPI-1/2 specifications and
-provides an object oriented interface which closely follows MPI-2 C++
-bindings. It supports point-to-point (sends, receives) and collective
-(broadcasts, scatters, gathers) communications of any picklable Python
-object, as well as optimized communications of Python object exposing
-the single-segment buffer interface (NumPy arrays, builtin
-bytes/string/array objects).
-
-On Anselm MPI4Py is available in standard Python modules.
-
-Modules
--------
-
-MPI4Py is build for OpenMPI. Before you start with MPI4Py you need to
-load Python and OpenMPI modules.
-
- $ module load python
- $ module load openmpi
-
-Execution
----------
-
-You need to import MPI to your python program. Include the following
-line to the python script:
-
- from mpi4py import MPI
-
-The MPI4Py enabled python programs [execute as any other
-OpenMPI](Running_OpenMPI.html) code.The simpliest way is
-to run
-
- $ mpiexec python <script>.py
-
-For example
-
- $ mpiexec python hello_world.py
-
-Examples
---------
-
-### Hello world!
-
- from mpi4py import MPI
-
- comm = MPI.COMM_WORLD
-
- print "Hello! I'm rank %d from %d running in total..." % (comm.rank, comm.size)
-
- comm.Barrier() Â # wait for everybody to synchronize
-
-###Collective Communication with NumPy arrays
-
- from mpi4py import MPI
- from __future__ import division
- import numpy as np
-
- comm = MPI.COMM_WORLD
-
- print("-"*78)
- print(" Running on %d cores" % comm.size)
- print("-"*78)
-
- comm.Barrier()
-
- # Prepare a vector of N=5 elements to be broadcasted...
- N = 5
- if comm.rank == 0:
- Â Â A = np.arange(N, dtype=np.float64) Â Â # rank 0 has proper data
- else:
- Â Â A = np.empty(N, dtype=np.float64) Â Â # all other just an empty array
-
- # Broadcast A from rank 0 to everybody
- comm.Bcast( [A, MPI.DOUBLE] )
-
- # Everybody should now have the same...
- print "[%02d] %s" % (comm.rank, A)
-
-Execute the above code as:
-
- $ qsub -q qexp -l select=4:ncpus=16:mpiprocs=16:ompthreads=1 -I
-
- $ module load python openmpi
-
- $ mpiexec -bycore -bind-to-core python hello_world.py
-
-In this example, we run MPI4Py enabled code on 4 nodes, 16 cores per
-node (total of 64 processes), each python process is bound to a
-different core.
-More examples and documentation can be found on [MPI for Python
-webpage](https://pythonhosted.org/mpi4py/usrman/index.html).
-
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-Running MPICH2
-==============
-
-
-
-MPICH2 program execution
-------------------------
-
-The MPICH2 programs use mpd daemon or ssh connection to spawn processes,
-no PBS support is needed. However the PBS allocation is required to
-access compute nodes. On Anselm, the **Intel MPI** and **mpich2 1.9**
-are MPICH2 based MPI implementations.
-
-### Basic usage
-
-Use the mpirun to execute the MPICH2 code.
-
-Example:
-
- $ qsub -q qexp -l select=4:ncpus=16 -I
- qsub: waiting for job 15210.srv11 to start
- qsub: job 15210.srv11 ready
-
- $ module load impi
-
- $ mpirun -ppn 1 -hostfile $PBS_NODEFILE ./helloworld_mpi.x
- Hello world! from rank 0 of 4 on host cn17
- Hello world! from rank 1 of 4 on host cn108
- Hello world! from rank 2 of 4 on host cn109
- Hello world! from rank 3 of 4 on host cn110
-
-In this example, we allocate 4 nodes via the express queue
-interactively. We set up the intel MPI environment and interactively run
-the helloworld_mpi.x program. We request MPI to spawn 1 process per
-node.
-Note that the executable helloworld_mpi.x must be available within the
-same path on all nodes. This is automatically fulfilled on the /home and
-/scratch filesystem.
-
-You need to preload the executable, if running on the local scratch
-/lscratch filesystem
-
- $ pwd
- /lscratch/15210.srv11
- $ mpirun -ppn 1 -hostfile $PBS_NODEFILE cp /home/username/helloworld_mpi.x .
- $ mpirun -ppn 1 -hostfile $PBS_NODEFILE ./helloworld_mpi.x
- Hello world! from rank 0 of 4 on host cn17
- Hello world! from rank 1 of 4 on host cn108
- Hello world! from rank 2 of 4 on host cn109
- Hello world! from rank 3 of 4 on host cn110
-
-In this example, we assume the executable helloworld_mpi.x is present
-on shared home directory. We run the cp command via mpirun, copying the
-executable from shared home to local scratch . Second mpirun will
-execute the binary in the /lscratch/15210.srv11 directory on nodes cn17,
-cn108, cn109 and cn110, one process per node.
-
-MPI process mapping may be controlled by PBS parameters.
-
-The mpiprocs and ompthreads parameters allow for selection of number of
-running MPI processes per node as well as number of OpenMP threads per
-MPI process.
-
-### One MPI process per node
-
-Follow this example to run one MPI process per node, 16 threads per
-process. Note that no options to mpirun are needed
-
- $ qsub -q qexp -l select=4:ncpus=16:mpiprocs=1:ompthreads=16 -I
-
- $ module load mvapich2
-
- $ mpirun ./helloworld_mpi.x
-
-In this example, we demonstrate recommended way to run an MPI
-application, using 1 MPI processes per node and 16 threads per socket,
-on 4 nodes.
-
-### Two MPI processes per node
-
-Follow this example to run two MPI processes per node, 8 threads per
-process. Note the options to mpirun for mvapich2. No options are needed
-for impi.
-
- $ qsub -q qexp -l select=4:ncpus=16:mpiprocs=2:ompthreads=8 -I
-
- $ module load mvapich2
-
- $ mpirun -bind-to numa ./helloworld_mpi.x
-
-In this example, we demonstrate recommended way to run an MPI
-application, using 2 MPI processes per node and 8 threads per socket,
-each process and its threads bound to a separate processor socket of the
-node, on 4 nodes
-
-### 16 MPI processes per node
-
-Follow this example to run 16 MPI processes per node, 1 thread per
-process. Note the options to mpirun for mvapich2. No options are needed
-for impi.
-
- $ qsub -q qexp -l select=4:ncpus=16:mpiprocs=16:ompthreads=1 -I
-
- $ module load mvapich2
-
- $ mpirun -bind-to core ./helloworld_mpi.x
-
-In this example, we demonstrate recommended way to run an MPI
-application, using 16 MPI processes per node, single threaded. Each
-process is bound to separate processor core, on 4 nodes.
-
-### OpenMP thread affinity
-
-Important! Bind every OpenMP thread to a core!
-
-In the previous two examples with one or two MPI processes per node, the
-operating system might still migrate OpenMP threads between cores. You
-might want to avoid this by setting these environment variable for GCC
-OpenMP:
-
- $ export GOMP_CPU_AFFINITY="0-15"
-
-or this one for Intel OpenMP:
-
- $ export KMP_AFFINITY=granularity=fine,compact,1,0
-
-As of OpenMP 4.0 (supported by GCC 4.9 and later and Intel 14.0 and
-later) the following variables may be used for Intel or GCC:
-
- $ export OMP_PROC_BIND=true
- $ export OMP_PLACES=cores
-
-Â
-
-MPICH2 Process Mapping and Binding
-----------------------------------
-
-The mpirun allows for precise selection of how the MPI processes will be
-mapped to the computational nodes and how these processes will bind to
-particular processor sockets and cores.
-
-### Machinefile
-
-Process mapping may be controlled by specifying a machinefile input to
-the mpirun program. Altough all implementations of MPI provide means for
-process mapping and binding, following examples are valid for the impi
-and mvapich2 only.
-
-Example machinefile
-
- cn110.bullx
- cn109.bullx
- cn108.bullx
- cn17.bullx
- cn108.bullx
-
-Use the machinefile to control process placement
-
- $ mpirun -machinefile machinefile helloworld_mpi.x
- Hello world! from rank 0 of 5 on host cn110
- Hello world! from rank 1 of 5 on host cn109
- Hello world! from rank 2 of 5 on host cn108
- Hello world! from rank 3 of 5 on host cn17
- Hello world! from rank 4 of 5 on host cn108
-
-In this example, we see that ranks have been mapped on nodes according
-to the order in which nodes show in the machinefile
-
-### Process Binding
-
-The Intel MPI automatically binds each process and its threads to the
-corresponding portion of cores on the processor socket of the node, no
-options needed. The binding is primarily controlled by environment
-variables. Read more about mpi process binding on [Intel
-website](https://software.intel.com/sites/products/documentation/hpc/ics/impi/41/lin/Reference_Manual/Environment_Variables_Process_Pinning.htm).
-The MPICH2 uses the -bind-to option Use -bind-to numa or -bind-to core
-to bind the process on single core or entire socket.
-
-### Bindings verification
-
-In all cases, binding and threading may be verified by executing
-
- $ mpirun -bindto numa numactl --show
- $ mpirun -bindto numa echo $OMP_NUM_THREADS
-
-Intel MPI on Xeon Phi
----------------------
-
-The[MPI section of Intel Xeon Phi
-chapter](../intel-xeon-phi.html) provides details on how
-to run Intel MPI code on Xeon Phi architecture.
-
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-Matlab
-======
-
-
-
-Introduction
-------------
-
-Matlab is available in versions R2015a and R2015b. There are always two
-variants of the release:
-
-- Non commercial or so called EDU variant, which can be used for
- common research and educational purposes.
-- Commercial or so called COM variant, which can used also for
- commercial activities. The licenses for commercial variant are much
- more expensive, so usually the commercial variant has only subset of
- features compared to the EDU available.
-
-Â
-
-To load the latest version of Matlab load the module
-
- $ module load MATLAB
-
-By default the EDU variant is marked as default. If you need other
-version or variant, load the particular version. To obtain the list of
-available versions use
-
- $ module avail MATLAB
-
-If you need to use the Matlab GUI to prepare your Matlab programs, you
-can use Matlab directly on the login nodes. But for all computations use
-Matlab on the compute nodes via PBS Pro scheduler.
-
-If you require the Matlab GUI, please follow the general informations
-about [running graphical
-applications](../../../get-started-with-it4innovations/accessing-the-clusters/graphical-user-interface/x-window-system/x-window-and-vnc.html).
-
-Matlab GUI is quite slow using the X forwarding built in the PBS (qsub
--X), so using X11 display redirection either via SSH or directly by
-xauth (please see the "GUI Applications on Compute Nodes over VNC" part
-[here](../../../get-started-with-it4innovations/accessing-the-clusters/graphical-user-interface/x-window-system/x-window-and-vnc.html))
-is recommended.
-
-To run Matlab with GUI, use
-
- $ matlab
-
-To run Matlab in text mode, without the Matlab Desktop GUI environment,
-use
-
- $ matlab -nodesktop -nosplash
-
-plots, images, etc... will be still available.
-
-Running parallel Matlab using Distributed Computing Toolbox / Engine
-------------------------------------------------------------------------
-
-Distributed toolbox is available only for the EDU variant
-
-The MPIEXEC mode available in previous versions is no longer available
-in MATLAB 2015. Also, the programming interface has changed. Refer
-to [Release
-Notes](http://www.mathworks.com/help/distcomp/release-notes.html#buanp9e-1).
-
-Delete previously used file mpiLibConf.m, we have observed crashes when
-using Intel MPI.
-
-To use Distributed Computing, you first need to setup a parallel
-profile. We have provided the profile for you, you can either import it
-in MATLAB command line:
-
- > parallel.importProfile('/apps/all/MATLAB/2015a-EDU/SalomonPBSPro.settings')
-
- ans =
-
- SalomonPBSProÂ
-
-Or in the GUI, go to tab HOME -> Parallel -> Manage Cluster
-Profiles..., click Import and navigate to :
-
-/apps/all/MATLAB/2015a-EDU/SalomonPBSPro.settings
-
-With the new mode, MATLAB itself launches the workers via PBS, so you
-can either use interactive mode or a batch mode on one node, but the
-actual parallel processing will be done in a separate job started by
-MATLAB itself. Alternatively, you can use "local" mode to run parallel
-code on just a single node.
-
-The profile is confusingly named Salomon, but you can use it also on
-Anselm.
-
-### Parallel Matlab interactive session
-
-Following example shows how to start interactive session with support
-for Matlab GUI. For more information about GUI based applications on
-Anselm see [this
-page](../../../get-started-with-it4innovations/accessing-the-clusters/graphical-user-interface/x-window-system/x-window-and-vnc.html).
-
- $ xhost +
- $ qsub -I -v DISPLAY=$(uname -n):$(echo $DISPLAY | cut -d ':' -f 2) -A NONE-0-0 -q qexp -l select=1 -l walltime=00:30:00
- -l feature__matlab__MATLAB=1
-
-This qsub command example shows how to run Matlab on a single node.
-
-The second part of the command shows how to request all necessary
-licenses. In this case 1 Matlab-EDU license and 48 Distributed Computing
-Engines licenses.
-
-Once the access to compute nodes is granted by PBS, user can load
-following modules and start Matlab:Â
-
- r1i0n17$ module load MATLAB/2015b-EDU
- r1i0n17$ matlab &
-
-### Parallel Matlab batch job in Local mode
-
-To run matlab in batch mode, write an matlab script, then write a bash
-jobscript and execute via the qsub command. By default, matlab will
-execute one matlab worker instance per allocated core.
-
- #!/bin/bash
- #PBS -A PROJECT ID
- #PBS -q qprod
- #PBS -l select=1:ncpus=16:mpiprocs=16:ompthreads=1
-
- # change to shared scratch directory
- SCR=/scratch/work/user/$USER/$PBS_JOBID
- mkdir -p $SCR ; cd $SCR || exit
-
- # copy input file to scratch
- cp $PBS_O_WORKDIR/matlabcode.m .
-
- # load modules
- module load MATLAB/2015a-EDU
-
- # execute the calculation
- matlab -nodisplay -r matlabcode > output.out
-
- # copy output file to home
- cp output.out $PBS_O_WORKDIR/.
-
-This script may be submitted directly to the PBS workload manager via
-the qsub command. The inputs and matlab script are in matlabcode.m
-file, outputs in output.out file. Note the missing .m extension in the
-matlab -r matlabcodefile call, **the .m must not be included**. Note
-that the **shared /scratch must be used**. Further, it is **important to
-include quit** statement at the end of the matlabcode.m script.
-
-Submit the jobscript using qsub
-
- $ qsub ./jobscript
-
-### Parallel Matlab Local mode program example
-
-The last part of the configuration is done directly in the user Matlab
-script before Distributed Computing Toolbox is started.
-
- cluster = parcluster('local')
-
-This script creates scheduler object "cluster" of type "local" that
-starts workers locally.Â
-
-Please note: Every Matlab script that needs to initialize/use matlabpool
-has to contain these three lines prior to calling parpool(sched, ...)
-function.Â
-
-The last step is to start matlabpool with "cluster" object and correct
-number of workers. We have 24 cores per node, so we start 24 workers.
-
- parpool(cluster,16);
-
-
- ... parallel code ...
-
-
- parpool close
-
-The complete example showing how to use Distributed Computing Toolbox in
-local mode is shown here.Â
-
- cluster = parcluster('local');
- cluster
-
- parpool(cluster,24);
-
- n=2000;
-
- W = rand(n,n);
- W = distributed(W);
- x = (1:n)';
- x = distributed(x);
- spmd
- [~, name] = system('hostname')
- Â Â Â
- Â Â Â T = W*x; % Calculation performed on labs, in parallel.
- Â Â Â Â Â Â Â Â Â Â Â Â % T and W are both codistributed arrays here.
- end
- T;
- whos        % T and W are both distributed arrays here.
-
- parpool close
- quit
-
-You can copy and paste the example in a .m file and execute. Note that
-the parpool size should correspond to **total number of cores**
-available on allocated nodes.
-
-### Parallel Matlab Batch job using PBS mode (workers spawned in a separate job)
-
-This mode uses PBS scheduler to launch the parallel pool. It uses the
-SalomonPBSPro profile that needs to be imported to Cluster Manager, as
-mentioned before. This methodod uses MATLAB's PBS Scheduler interface -
-it spawns the workers in a separate job submitted by MATLAB using qsub.
-
-This is an example of m-script using PBS mode:
-
- cluster = parcluster('SalomonPBSPro');
- set(cluster, 'SubmitArguments', '-A OPEN-0-0');
- set(cluster, 'ResourceTemplate', '-q qprod -l select=10:ncpus=16');
- set(cluster, 'NumWorkers', 160);
-
- pool = parpool(cluster, 160);
-
- n=2000;
-
- W = rand(n,n);
- W = distributed(W);
- x = (1:n)';
- x = distributed(x);
- spmd
- [~, name] = system('hostname')
-
- T = W*x; % Calculation performed on labs, in parallel.
- % T and W are both codistributed arrays here.
- end
- whos % T and W are both distributed arrays here.
-
- % shut down parallel pool
- delete(pool)
-
-Note that we first construct a cluster object using the imported
-profile, then set some important options, namely : SubmitArguments,
-where you need to specify accounting id, and ResourceTemplate, where you
-need to specify number of nodes to run the job.Â
-
-You can start this script using batch mode the same way as in Local mode
-example.
-
-### Parallel Matlab Batch with direct launch (workers spawned within the existing job)
-
-This method is a "hack" invented by us to emulate the mpiexec
-functionality found in previous MATLAB versions. We leverage the MATLAB
-Generic Scheduler interface, but instead of submitting the workers to
-PBS, we launch the workers directly within the running job, thus we
-avoid the issues with master script and workers running in separate jobs
-(issues with license not available, waiting for the worker's job to
-spawn etc.)
-
-Please note that this method is experimental.
-
-For this method, you need to use SalomonDirect profile, import it
-using [the same way as
-SalomonPBSPro](copy_of_matlab.html#running-parallel-matlab-using-distributed-computing-toolbox---engine)Â
-
-This is an example of m-script using direct mode:
-
- parallel.importProfile('/apps/all/MATLAB/2015a-EDU/SalomonDirect.settings')
- cluster = parcluster('SalomonDirect');
- set(cluster, 'NumWorkers', 48);
-
- pool = parpool(cluster, 48);
-
- n=2000;
-
- W = rand(n,n);
- W = distributed(W);
- x = (1:n)';
- x = distributed(x);
- spmd
- [~, name] = system('hostname')
-
- T = W*x; % Calculation performed on labs, in parallel.
- % T and W are both codistributed arrays here.
- end
- whos % T and W are both distributed arrays here.
-
- % shut down parallel pool
- delete(pool)
-
-### Non-interactive Session and Licenses
-
-If you want to run batch jobs with Matlab, be sure to request
-appropriate license features with the PBS Pro scheduler, at least the "
--l __feature__matlab__MATLAB=1" for EDU variant of Matlab. More
-information about how to check the license features states and how to
-request them with PBS Pro, please [look
-here](../isv_licenses.html).
-
-In case of non-interactive session please read the [following
-information](../isv_licenses.html) on how to modify the
-qsub command to test for available licenses prior getting the resource
-allocation.
-
-### Matlab Distributed Computing Engines start up time
-
-Starting Matlab workers is an expensive process that requires certain
-amount of time. For your information please see the following table:
-
- |compute nodes|number of workers|start-up time[s]|
- |---|---|---|
- |16|384|831|
- |8|192|807|
- |4|96|483|
- |2|48|16|
-
-MATLAB on UV2000Â
------------------
-
-UV2000 machine available in queue "qfat" can be used for MATLAB
-computations. This is a SMP NUMA machine with large amount of RAM, which
-can be beneficial for certain types of MATLAB jobs. CPU cores are
-allocated in chunks of 8 for this machine.
-
-You can use MATLAB on UV2000 in two parallel modes :
-
-### Threaded mode
-
-Since this is a SMP machine, you can completely avoid using Parallel
-Toolbox and use only MATLAB's threading. MATLAB will automatically
-detect the number of cores you have allocated and will setÂ
-maxNumCompThreads accordingly and certain
-operations, such as fft, , eig, svd,
-etc. will be automatically run in threads. The advantage of this mode is
-that you don't need to modify your existing sequential codes.
-
-### Local cluster mode
-
-You can also use Parallel Toolbox on UV2000. Use l[ocal cluster
-mode](copy_of_matlab.html#parallel-matlab-batch-job-in-local-mode),
-"SalomonPBSPro" profile will not work.
-
-Â
-
-Â
-
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+++ /dev/null
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-Numerical languages
-===================
-
-Interpreted languages for numerical computations and analysis
-
-
-
-Introduction
-------------
-
-This section contains a collection of high-level interpreted languages,
-primarily intended for numerical computations.
-
-Matlab
-------
-
-MATLAB®^ is a high-level language and interactive environment for
-numerical computation, visualization, and programming.
-
- $ module load MATLAB/2015b-EDU
- $ matlab
-
-Read more at the [Matlab
-page](matlab.html).
-
-Octave
-------
-
-GNU Octave is a high-level interpreted language, primarily intended for
-numerical computations. The Octave language is quite similar to Matlab
-so that most programs are easily portable.
-
- $ module load Octave
- $ octave
-
-Read more at the [Octave page](octave.html).
-
-R
--
-
-The R is an interpreted language and environment for statistical
-computing and graphics.
-
- $ module load R
- $ R
-
-Read more at the [R page](r.html).
-
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-Matlab 2013-2014
-================
-
-
-
-Introduction
-------------
-
-This document relates to the old versions R2013 and R2014. For MATLAB
-2015, please use [this documentation
-instead](copy_of_matlab.html).
-
-Matlab is available in the latest stable version. There are always two
-variants of the release:
-
-- Non commercial or so called EDU variant, which can be used for
- common research and educational purposes.
-- Commercial or so called COM variant, which can used also for
- commercial activities. The licenses for commercial variant are much
- more expensive, so usually the commercial variant has only subset of
- features compared to the EDU available.
-
-Â
-
-To load the latest version of Matlab load the module
-
- $ module load matlab
-
-By default the EDU variant is marked as default. If you need other
-version or variant, load the particular version. To obtain the list of
-available versions use
-
- $ module avail matlab
-
-If you need to use the Matlab GUI to prepare your Matlab programs, you
-can use Matlab directly on the login nodes. But for all computations use
-Matlab on the compute nodes via PBS Pro scheduler.
-
-If you require the Matlab GUI, please follow the general informations
-about [running graphical
-applications](https://docs.it4i.cz/anselm-cluster-documentation/software/numerical-languages/resolveuid/11e53ad0d2fd4c5187537f4baeedff33).
-
-Matlab GUI is quite slow using the X forwarding built in the PBS (qsub
--X), so using X11 display redirection either via SSH or directly by
-xauth (please see the "GUI Applications on Compute Nodes over VNC" part
-[here](https://docs.it4i.cz/anselm-cluster-documentation/software/numerical-languages/resolveuid/11e53ad0d2fd4c5187537f4baeedff33))
-is recommended.
-
-To run Matlab with GUI, use
-
- $ matlab
-
-To run Matlab in text mode, without the Matlab Desktop GUI environment,
-use
-
- $ matlab -nodesktop -nosplash
-
-plots, images, etc... will be still available.
-
-Running parallel Matlab using Distributed Computing Toolbox / Engine
---------------------------------------------------------------------
-
-Recommended parallel mode for running parallel Matlab on Anselm is
-MPIEXEC mode. In this mode user allocates resources through PBS prior to
-starting Matlab. Once resources are granted the main Matlab instance is
-started on the first compute node assigned to job by PBS and workers are
-started on all remaining nodes. User can use both interactive and
-non-interactive PBS sessions. This mode guarantees that the data
-processing is not performed on login nodes, but all processing is on
-compute nodes.
-
-Â 
-
-For the performance reasons Matlab should use system MPI. On Anselm the
-supported MPI implementation for Matlab is Intel MPI. To switch to
-system MPI user has to override default Matlab setting by creating new
-configuration file in its home directory. The path and file name has to
-be exactly the same as in the following listing:
-
- $ vim ~/matlab/mpiLibConf.m
-
- function [lib, extras] = mpiLibConf
- %MATLAB MPI Library overloading for Infiniband Networks
-
- mpich = '/opt/intel/impi/4.1.1.036/lib64/';
-
- disp('Using Intel MPI 4.1.1.036 over Infiniband')
-
- lib = strcat(mpich, 'libmpich.so');
- mpl = strcat(mpich, 'libmpl.so');
- opa = strcat(mpich, 'libopa.so');
-
- extras = {};
-
-System MPI library allows Matlab to communicate through 40Gbps
-Infiniband QDR interconnect instead of slower 1Gb ethernet network.
-
-Please note: The path to MPI library in "mpiLibConf.m" has to match with
-version of loaded Intel MPI module. In this example the version
-4.1.1.036 of Iintel MPI is used by Matlab and therefore module
-impi/4.1.1.036Â has to be loaded prior to starting Matlab.
-
-### Parallel Matlab interactive session
-
-Once this file is in place, user can request resources from PBS.
-Following example shows how to start interactive session with support
-for Matlab GUI. For more information about GUI based applications on
-Anselm see [this
-page](https://docs.it4i.cz/anselm-cluster-documentation/software/numerical-languages/resolveuid/11e53ad0d2fd4c5187537f4baeedff33).
-
- $ xhost +
- $ qsub -I -v DISPLAY=$(uname -n):$(echo $DISPLAY | cut -d ':' -f 2) -A NONE-0-0 -q qexp -l select=4:ncpus=16:mpiprocs=16 -l walltime=00:30:00
- -l feature__matlab__MATLAB=1
-
-This qsub command example shows how to run Matlab with 32 workers in
-following configuration: 2 nodes (use all 16 cores per node) and 16
-workers = mpirocs per node (-l select=2:ncpus=16:mpiprocs=16). If user
-requires to run smaller number of workers per node then the "mpiprocs"
-parameter has to be changed.
-
-The second part of the command shows how to request all necessary
-licenses. In this case 1 Matlab-EDU license and 32 Distributed Computing
-Engines licenses.
-
-Once the access to compute nodes is granted by PBS, user can load
-following modules and start Matlab:Â
-
- cn79$ module load matlab/R2013a-EDU
- cn79$ module load impi/4.1.1.036
- cn79$ matlab &
-
-### Parallel Matlab batch job
-
-To run matlab in batch mode, write an matlab script, then write a bash
-jobscript and execute via the qsub command. By default, matlab will
-execute one matlab worker instance per allocated core.
-
- #!/bin/bash
- #PBS -A PROJECT ID
- #PBS -q qprod
- #PBS -l select=2:ncpus=16:mpiprocs=16:ompthreads=1
-
- # change to shared scratch directory
- SCR=/scratch/$USER/$PBS_JOBID
- mkdir -p $SCR ; cd $SCR || exit
-
- # copy input file to scratch
- cp $PBS_O_WORKDIR/matlabcode.m .
-
- # load modules
- module load matlab/R2013a-EDU
- module load impi/4.1.1.036
-
- # execute the calculation
- matlab -nodisplay -r matlabcode > output.out
-
- # copy output file to home
- cp output.out $PBS_O_WORKDIR/.
-
-This script may be submitted directly to the PBS workload manager via
-the qsub command. The inputs and matlab script are in matlabcode.m
-file, outputs in output.out file. Note the missing .m extension in the
-matlab -r matlabcodefile call, **the .m must not be included**. Note
-that the **shared /scratch must be used**. Further, it is **important to
-include quit** statement at the end of the matlabcode.m script.
-
-Submit the jobscript using qsub
-
- $ qsub ./jobscript
-
-### Parallel Matlab program example
-
-The last part of the configuration is done directly in the user Matlab
-script before Distributed Computing Toolbox is started.
-
- sched = findResource('scheduler', 'type', 'mpiexec');
- set(sched, 'MpiexecFileName', '/apps/intel/impi/4.1.1/bin/mpirun');
- set(sched, 'EnvironmentSetMethod', 'setenv');
-
-This script creates scheduler object "sched" of type "mpiexec" that
-starts workers using mpirun tool. To use correct version of mpirun, the
-second line specifies the path to correct version of system Intel MPI
-library.
-
-Please note: Every Matlab script that needs to initialize/use matlabpool
-has to contain these three lines prior to calling matlabpool(sched, ...)
-function.Â
-
-The last step is to start matlabpool with "sched" object and correct
-number of workers. In this case qsub asked for total number of 32 cores,
-therefore the number of workers is also set to 32.
-
- matlabpool(sched,32);
-
-
- ... parallel code ...
-
-
- matlabpool close
-
-The complete example showing how to use Distributed Computing Toolbox is
-show here.Â
-
- sched = findResource('scheduler', 'type', 'mpiexec');
- set(sched, 'MpiexecFileName', '/apps/intel/impi/4.1.1/bin/mpirun')
- set(sched, 'EnvironmentSetMethod', 'setenv')
- set(sched, 'SubmitArguments', '')
- sched
-
- matlabpool(sched,32);
-
- n=2000;
-
- W = rand(n,n);
- W = distributed(W);
- x = (1:n)';
- x = distributed(x);
- spmd
- [~, name] = system('hostname')
- Â Â Â
- Â Â Â T = W*x; % Calculation performed on labs, in parallel.
- Â Â Â Â Â Â Â Â Â Â Â Â % T and W are both codistributed arrays here.
- end
- T;
- whos        % T and W are both distributed arrays here.
-
- matlabpool close
- quit
-
-You can copy and paste the example in a .m file and execute. Note that
-the matlabpool size should correspond to **total number of cores**
-available on allocated nodes.
-
-### Non-interactive Session and Licenses
-
-If you want to run batch jobs with Matlab, be sure to request
-appropriate license features with the PBS Pro scheduler, at least the "
--l __feature__matlab__MATLAB=1" for EDU variant of Matlab. More
-information about how to check the license features states and how to
-request them with PBS Pro, please [look
-here](../isv_licenses.html).
-
-In case of non-interactive session please read the [following
-information](../isv_licenses.html) on how to modify the
-qsub command to test for available licenses prior getting the resource
-allocation.
-
-### Matlab Distributed Computing Engines start up time
-
-Starting Matlab workers is an expensive process that requires certain
-amount of time. For your information please see the following table:
-
- |compute nodes|number of workers|start-up time[s]|
- |---|---|---|
- 16 256 1008
- 8 128 534
- 4 64 333
- 2 32 210
-
-Â
-
-Â
-
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-Octave
-======
-
-
-
-Introduction
-------------
-
-GNU Octave is a high-level interpreted language, primarily intended for
-numerical computations. It provides capabilities for the numerical
-solution of linear and nonlinear problems, and for performing other
-numerical experiments. It also provides extensive graphics capabilities
-for data visualization and manipulation. Octave is normally used through
-its interactive command line interface, but it can also be used to write
-non-interactive programs. The Octave language is quite similar to Matlab
-so that most programs are easily portable. Read more on
-<http://www.gnu.org/software/octave/>***
-
-Two versions of octave are available on Anselm, via module
-
- Version module
- ----------------------------------------------------- |---|---|-----------------
- Octave 3.8.2, compiled with GCC and Multithreaded MKL Octave/3.8.2-gimkl-2.11.5
- Octave 4.0.1, compiled with GCC and Multithreaded MKL Octave/4.0.1-gimkl-2.11.5
- Octave 4.0.0, compiled with >GCC and OpenBLAS Octave/4.0.0-foss-2015g
-
-Â Modules and execution
-----------------------
-
- $ module load Octave
-
-The octave on Anselm is linked to highly optimized MKL mathematical
-library. This provides threaded parallelization to many octave kernels,
-notably the linear algebra subroutines. Octave runs these heavy
-calculation kernels without any penalty. By default, octave would
-parallelize to 16 threads. You may control the threads by setting the
-OMP_NUM_THREADS environment variable.
-
-To run octave interactively, log in with ssh -X parameter for X11
-forwarding. Run octave:
-
- $ octave
-
-To run octave in batch mode, write an octave script, then write a bash
-jobscript and execute via the qsub command. By default, octave will use
-16 threads when running MKL kernels.
-
- #!/bin/bash
-
- # change to local scratch directory
- cd /lscratch/$PBS_JOBID || exit
-
- # copy input file to scratch
- cp $PBS_O_WORKDIR/octcode.m .
-
- # load octave module
- module load octave
-
- # execute the calculation
- octave -q --eval octcode > output.out
-
- # copy output file to home
- cp output.out $PBS_O_WORKDIR/.
-
- #exit
- exit
-
-This script may be submitted directly to the PBS workload manager via
-the qsub command. The inputs are in octcode.m file, outputs in
-output.out file. See the single node jobscript example in the [Job
-execution
-section](http://support.it4i.cz/docs/anselm-cluster-documentation/resource-allocation-and-job-execution).
-
-The octave c compiler mkoctfile calls the GNU gcc 4.8.1 for compiling
-native c code. This is very useful for running native c subroutines in
-octave environment.
-
- $ mkoctfile -v
-
-Octave may use MPI for interprocess communication
-This functionality is currently not supported on Anselm cluster. In case
-you require the octave interface to MPI, please contact [Anselm
-support](https://support.it4i.cz/rt/).
-
-Xeon Phi Support
-----------------
-
-Octave may take advantage of the Xeon Phi accelerators. This will only
-work on the [Intel Xeon Phi](../intel-xeon-phi.html)
-[accelerated nodes](../../compute-nodes.html).
-
-### Automatic offload support
-
-Octave can accelerate BLAS type operations (in particular the Matrix
-Matrix multiplications] on the Xeon Phi accelerator, via [Automatic
-Offload using the MKL
-library](../intel-xeon-phi.html#section-3)
-
-Example
-
- $ export OFFLOAD_REPORT=2
- $ export MKL_MIC_ENABLE=1
- $ module load octave
- $ octave -q
- octave:1> A=rand(10000); B=rand(10000);
- octave:2> tic; C=A*B; toc
- [MKL] [MIC --] [AO Function]Â Â Â DGEMM
- [MKL] [MIC --] [AO DGEMM Workdivision]Â Â Â 0.32 0.68
- [MKL] [MIC 00] [AO DGEMM CPU Time]Â Â Â 2.896003 seconds
- [MKL] [MIC 00] [AO DGEMM MIC Time]Â Â Â 1.967384 seconds
- [MKL] [MIC 00] [AO DGEMM CPU->MIC Data]Â Â Â 1347200000 bytes
- [MKL] [MIC 00] [AO DGEMM MIC->CPU Data]Â Â Â 2188800000 bytes
- Elapsed time is 2.93701 seconds.
-
-In this example, the calculation was automatically divided among the CPU
-cores and the Xeon Phi MIC accelerator, reducing the total runtime from
-6.3 secs down to 2.9 secs.
-
-### Native support
-
-A version of [native](../intel-xeon-phi.html#section-4)
-Octave is compiled for Xeon Phi accelerators. Some limitations apply for
-this version:
-
-- Only command line support. GUI, graph plotting etc. is
- not supported.
-- Command history in interactive mode is not supported.
-
-Octave is linked with parallel Intel MKL, so it best suited for batch
-processing of tasks that utilize BLAS, LAPACK and FFT operations. By
-default, number of threads is set to 120, you can control this
-with > OMP_NUM_THREADS environment
-variable.
-
-Calculations that do not employ parallelism (either by using parallel
-MKL eg. via matrix operations, fork()
-function, [parallel
-package](http://octave.sourceforge.net/parallel/) or
-other mechanism) will actually run slower than on host CPU.
-
-To use Octave on a node with Xeon Phi:
-
- $ ssh mic0 # login to the MIC card
- $ source /apps/tools/octave/3.8.2-mic/bin/octave-env.sh # set up environment variables
- $ octave -q /apps/tools/octave/3.8.2-mic/example/test0.m # run an exampleÂ
-
-Â
-
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-R
-=
-
-
-
-Introduction
-------------
-
-The R is a language and environment for statistical computing and
-graphics. R provides a wide variety of statistical (linear and
-nonlinear modelling, classical statistical tests, time-series analysis,
-classification, clustering, ...) and graphical techniques, and is highly
-extensible.
-
-One of R's strengths is the ease with which well-designed
-publication-quality plots can be produced, including mathematical
-symbols and formulae where needed. Great care has been taken over the
-defaults for the minor design choices in graphics, but the user retains
-full control.
-
-Another convenience is the ease with which the C code or third party
-libraries may be integrated within R.
-
-Extensive support for parallel computing is available within R.
-
-Read more on <http://www.r-project.org/>,
-<http://cran.r-project.org/doc/manuals/r-release/R-lang.html>
-
-Modules
--------
-
-**The R version 3.0.1 is available on Anselm, along with GUI interface
-Rstudio
-
- |Application|Version|module|
- ------- |---|---|---- ---------
- **R** R 3.0.1 R
- |**Rstudio**|Rstudio 0.97|Rstudio|
-
- $ module load R
-
-Execution
----------
-
-The R on Anselm is linked to highly optimized MKL mathematical
-library. This provides threaded parallelization to many R kernels,
-notably the linear algebra subroutines. The R runs these heavy
-calculation kernels without any penalty. By default, the R would
-parallelize to 16 threads. You may control the threads by setting the
-OMP_NUM_THREADS environment variable.
-
-### Interactive execution
-
-To run R interactively, using Rstudio GUI, log in with ssh -X parameter
-for X11 forwarding. Run rstudio:
-
- $ module load Rstudio
- $ rstudio
-
-### Batch execution
-
-To run R in batch mode, write an R script, then write a bash jobscript
-and execute via the qsub command. By default, R will use 16 threads when
-running MKL kernels.
-
-Example jobscript:
-
- #!/bin/bash
-
- # change to local scratch directory
- cd /lscratch/$PBS_JOBID || exit
-
- # copy input file to scratch
- cp $PBS_O_WORKDIR/rscript.R .
-
- # load R module
- module load R
-
- # execute the calculation
- R CMD BATCH rscript.R routput.out
-
- # copy output file to home
- cp routput.out $PBS_O_WORKDIR/.
-
- #exit
- exit
-
-This script may be submitted directly to the PBS workload manager via
-the qsub command. The inputs are in rscript.R file, outputs in
-routput.out file. See the single node jobscript example in the [Job
-execution
-section](../../resource-allocation-and-job-execution/job-submission-and-execution.html).
-
-Parallel R
-----------
-
-Parallel execution of R may be achieved in many ways. One approach is
-the implied parallelization due to linked libraries or specially enabled
-functions, as [described
-above](r.html#interactive-execution). In the following
-sections, we focus on explicit parallelization, where parallel
-constructs are directly stated within the R script.
-
-Package parallel
---------------------
-
-The package parallel provides support for parallel computation,
-including by forking (taken from package multicore), by sockets (taken
-from package snow) and random-number generation.
-
-The package is activated this way:
-
- $ R
- > library(parallel)
-
-More information and examples may be obtained directly by reading the
-documentation available in R
-
- > ?parallel
- > library(help = "parallel")
- > vignette("parallel")
-
-Download the package
-[parallell](package-parallel-vignette) vignette.
-
-The forking is the most simple to use. Forking family of functions
-provide parallelized, drop in replacement for the serial apply() family
-of functions.
-
-Forking via package parallel provides functionality similar to OpenMP
-construct
-#omp parallel for
-
-Only cores of single node can be utilized this way!
-
-Forking example:
-
- library(parallel)
-
- #integrand function
- f <- function(i,h) {
- x <- h*(i-0.5)
- return (4/(1 + x*x))
- }
-
- #initialize
- size <- detectCores()
-
- while (TRUE)
- {
- #read number of intervals
- cat("Enter the number of intervals: (0 quits) ")
- fp<-file("stdin"); n<-scan(fp,nmax=1); close(fp)
-
- if(n<=0) break
-
- #run the calculation
- n <- max(n,size)
- h <- 1.0/n
-
- i <- seq(1,n);
- pi3 <- h*sum(simplify2array(mclapply(i,f,h,mc.cores=size)));
-
- #print results
- cat(sprintf("Value of PI %16.14f, diff= %16.14fn",pi3,pi3-pi))
- }
-
-The above example is the classic parallel example for calculating the
-number π. Note the **detectCores()** and **mclapply()** functions.
-Execute the example as:
-
- $ R --slave --no-save --no-restore -f pi3p.R
-
-Every evaluation of the integrad function runs in parallel on different
-process.
-
-Package Rmpi
-------------
-
-package Rmpi provides an interface (wrapper) to MPI APIs.
-
-It also provides interactive R slave environment. On Anselm, Rmpi
-provides interface to the
-[OpenMPI](../mpi-1/Running_OpenMPI.html).
-
-Read more on Rmpi at <http://cran.r-project.org/web/packages/Rmpi/>,
-reference manual is available at
-<http://cran.r-project.org/web/packages/Rmpi/Rmpi.pdf>
-
-When using package Rmpi, both openmpi and R modules must be loaded
-
- $ module load openmpi
- $ module load R
-
-Rmpi may be used in three basic ways. The static approach is identical
-to executing any other MPI programm. In addition, there is Rslaves
-dynamic MPI approach and the mpi.apply approach. In the following
-section, we will use the number π integration example, to illustrate all
-these concepts.
-
-### static Rmpi
-
-Static Rmpi programs are executed via mpiexec, as any other MPI
-programs. Number of processes is static - given at the launch time.
-
-Static Rmpi example:
-
- library(Rmpi)
-
- #integrand function
- f <- function(i,h) {
- x <- h*(i-0.5)
- return (4/(1 + x*x))
- }
-
- #initialize
- invisible(mpi.comm.dup(0,1))
- rank <- mpi.comm.rank()
- size <- mpi.comm.size()
- n<-0
-
- while (TRUE)
- {
- #read number of intervals
- if (rank==0) {
- cat("Enter the number of intervals: (0 quits) ")
- fp<-file("stdin"); n<-scan(fp,nmax=1); close(fp)
- }
-
- #broadcat the intervals
- n <- mpi.bcast(as.integer(n),type=1)
-
- if(n<=0) break
-
- #run the calculation
- n <- max(n,size)
- h <- 1.0/n
-
- i <- seq(rank+1,n,size);
- mypi <- h*sum(sapply(i,f,h));
-
- pi3 <- mpi.reduce(mypi)
-
- #print results
- if (rank==0) cat(sprintf("Value of PI %16.14f, diff= %16.14fn",pi3,pi3-pi))
- }
-
- mpi.quit()
-
-The above is the static MPI example for calculating the number π. Note
-the **library(Rmpi)** and **mpi.comm.dup()** function calls.
-Execute the example as:
-
- $ mpiexec R --slave --no-save --no-restore -f pi3.R
-
-### dynamic Rmpi
-
-Dynamic Rmpi programs are executed by calling the R directly. openmpi
-module must be still loaded. The R slave processes will be spawned by a
-function call within the Rmpi program.
-
-Dynamic Rmpi example:
-
- #integrand function
- f <- function(i,h) {
- x <- h*(i-0.5)
- return (4/(1 + x*x))
- }
-
- #the worker function
- workerpi <- function()
- {
- #initialize
- rank <- mpi.comm.rank()
- size <- mpi.comm.size()
- n<-0
-
- while (TRUE)
- {
- #read number of intervals
- if (rank==0) {
- cat("Enter the number of intervals: (0 quits) ")
- fp<-file("stdin"); n<-scan(fp,nmax=1); close(fp)
- }
-
- #broadcat the intervals
- n <- mpi.bcast(as.integer(n),type=1)
-
- if(n<=0) break
-
- #run the calculation
- n <- max(n,size)
- h <- 1.0/n
-
- i <- seq(rank+1,n,size);
- mypi <- h*sum(sapply(i,f,h));
-
- pi3 <- mpi.reduce(mypi)
-
- #print results
- if (rank==0) cat(sprintf("Value of PI %16.14f, diff= %16.14fn",pi3,pi3-pi))
- }
- }
-
- #main
- library(Rmpi)
-
- cat("Enter the number of slaves: ")
- fp<-file("stdin"); ns<-scan(fp,nmax=1); close(fp)
-
- mpi.spawn.Rslaves(nslaves=ns)
- mpi.bcast.Robj2slave(f)
- mpi.bcast.Robj2slave(workerpi)
-
- mpi.bcast.cmd(workerpi())
- workerpi()
-
- mpi.quit()
-
-The above example is the dynamic MPI example for calculating the number
-Ď€. Both master and slave processes carry out the calculation. Note the
-mpi.spawn.Rslaves(), mpi.bcast.Robj2slave()** and the
-mpi.bcast.cmd()** function calls.
-Execute the example as:
-
- $ R --slave --no-save --no-restore -f pi3Rslaves.R
-
-### mpi.apply Rmpi
-
-mpi.apply is a specific way of executing Dynamic Rmpi programs.
-
-mpi.apply() family of functions provide MPI parallelized, drop in
-replacement for the serial apply() family of functions.
-
-Execution is identical to other dynamic Rmpi programs.
-
-mpi.apply Rmpi example:
-
- #integrand function
- f <- function(i,h) {
- x <- h*(i-0.5)
- return (4/(1 + x*x))
- }
-
- #the worker function
- workerpi <- function(rank,size,n)
- {
- #run the calculation
- n <- max(n,size)
- h <- 1.0/n
-
- i <- seq(rank,n,size);
- mypi <- h*sum(sapply(i,f,h));
-
- return(mypi)
- }
-
- #main
- library(Rmpi)
-
- cat("Enter the number of slaves: ")
- fp<-file("stdin"); ns<-scan(fp,nmax=1); close(fp)
-
- mpi.spawn.Rslaves(nslaves=ns)
- mpi.bcast.Robj2slave(f)
- mpi.bcast.Robj2slave(workerpi)
-
- while (TRUE)
- {
- #read number of intervals
- cat("Enter the number of intervals: (0 quits) ")
- fp<-file("stdin"); n<-scan(fp,nmax=1); close(fp)
- if(n<=0) break
-
- #run workerpi
- i=seq(1,2*ns)
- pi3=sum(mpi.parSapply(i,workerpi,2*ns,n))
-
- #print results
- cat(sprintf("Value of PI %16.14f, diff= %16.14fn",pi3,pi3-pi))
- }
-
- mpi.quit()
-
-The above is the mpi.apply MPI example for calculating the number π.
-Only the slave processes carry out the calculation. Note the
-mpi.parSapply(), ** function call. The package
-parallel
-[example](r.html#package-parallel)[above](r.html#package-parallel){.anchor
-may be trivially adapted (for much better performance) to this structure
-using the mclapply() in place of mpi.parSapply().
-
-Execute the example as:
-
- $ R --slave --no-save --no-restore -f pi3parSapply.R
-
-Combining parallel and Rmpi
----------------------------
-
-Currently, the two packages can not be combined for hybrid calculations.
-
-Parallel execution
-------------------
-
-The R parallel jobs are executed via the PBS queue system exactly as any
-other parallel jobs. User must create an appropriate jobscript and
-submit via the **qsub**
-
-Example jobscript for [static Rmpi](r.html#static-rmpi)
-parallel R execution, running 1 process per core:
-
- #!/bin/bash
- #PBS -q qprod
- #PBS -N Rjob
- #PBS -l select=100:ncpus=16:mpiprocs=16:ompthreads=1
-
- # change to scratch directory
- SCRDIR=/scratch/$USER/myjob
- cd $SCRDIR || exit
-
- # copy input file to scratch
- cp $PBS_O_WORKDIR/rscript.R .
-
- # load R and openmpi module
- module load R
- module load openmpi
-
- # execute the calculation
- mpiexec -bycore -bind-to-core R --slave --no-save --no-restore -f rscript.R
-
- # copy output file to home
- cp routput.out $PBS_O_WORKDIR/.
-
- #exit
- exit
-
-For more information about jobscripts and MPI execution refer to the
-[Job
-submission](../../resource-allocation-and-job-execution/job-submission-and-execution.html)
-and general [MPI](../mpi-1.html) sections.
-
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-FFTW
-====
-
-The discrete Fourier transform in one or more dimensions, MPI parallel
-
-
-
-Â
-
-FFTW is a C subroutine library for computing the discrete Fourier
-transform in one or more dimensions, of arbitrary input size, and of
-both real and complex data (as well as of even/odd data, i.e. the
-discrete cosine/sine transforms or DCT/DST). The FFTW library allows for
-MPI parallel, in-place discrete Fourier transform, with data distributed
-over number of nodes.
-
-Two versions, **3.3.3** and **2.1.5** of FFTW are available on Anselm,
-each compiled for **Intel MPI** and **OpenMPI** using **intel** and
-gnu** compilers. These are available via modules:
-
-<col width="25%" />
-<col width="25%" />
-<col width="25%" />
-<col width="25%" />
- |Version |Parallelization |module |linker options |
- | --- | --- |
- |FFTW3 gcc3.3.3 |pthread, OpenMP |fftw3/3.3.3-gcc |-lfftw3, -lfftw3_threads-lfftw3_omp |
- |FFTW3 icc3.3.3\ |pthread, OpenMP |fftw3 |-lfftw3, -lfftw3_threads-lfftw3_omp |
- |FFTW2 gcc2.1.5\ |pthread |fftw2/2.1.5-gcc |-lfftw, -lfftw_threads |
- |FFTW2 icc2.1.5 |pthread |fftw2 |-lfftw, -lfftw_threads |
- |FFTW3 gcc3.3.3 |OpenMPI |fftw-mpi3/3.3.3-gcc |-lfftw3_mpi |
- |FFTW3 icc3.3.3 |Intel MPI |fftw3-mpi |-lfftw3_mpi |
- |FFTW2 gcc2.1.5 |OpenMPI |fftw2-mpi/2.1.5-gcc |-lfftw_mpi |
- |FFTW2 gcc2.1.5 |IntelMPI |fftw2-mpi/2.1.5-gcc |-lfftw_mpi |
-
- $ module load fftw3
-
-The module sets up environment variables, required for linking and
-running fftw enabled applications. Make sure that the choice of fftw
-module is consistent with your choice of MPI library. Mixing MPI of
-different implementations may have unpredictable results.
-
-Example
--------
-
- #include <fftw3-mpi.h>
- int main(int argc, char **argv)
- {
- Â Â Â const ptrdiff_t N0 = 100, N1 = 1000;
- Â Â Â fftw_plan plan;
- Â Â Â fftw_complex *data;
- Â Â Â ptrdiff_t alloc_local, local_n0, local_0_start, i, j;
-
- Â Â Â MPI_Init(&argc, &argv);
- Â Â Â fftw_mpi_init();
-
- Â Â Â /* get local data size and allocate */
- Â Â Â alloc_local = fftw_mpi_local_size_2d(N0, N1, MPI_COMM_WORLD,
- Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â &local_n0, &local_0_start);
- Â Â Â data = fftw_alloc_complex(alloc_local);
-
- Â Â Â /* create plan for in-place forward DFT */
- Â Â Â plan = fftw_mpi_plan_dft_2d(N0, N1, data, data, MPI_COMM_WORLD,
- Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â FFTW_FORWARD, FFTW_ESTIMATE);
-
- Â Â Â /* initialize data */
- Â Â Â for (i = 0; i < local_n0; ++i) for (j = 0; j < N1; ++j)
- Â Â Â {Â Â data[i*N1 + j][0] = i;
- Â Â Â Â Â Â Â data[i*N1 + j][1] = j; }
-
- Â Â Â /* compute transforms, in-place, as many times as desired */
- Â Â Â fftw_execute(plan);
-
- Â Â Â fftw_destroy_plan(plan);
-
- Â Â Â MPI_Finalize();
- }
-
-Load modules and compile:
-
- $ module load impi intel
- $ module load fftw3-mpi
-
- $ mpicc testfftw3mpi.c -o testfftw3mpi.x -Wl,-rpath=$LIBRARY_PATH -lfftw3_mpi
-
- Run the example as [Intel MPI
-program](../mpi-1/running-mpich2.html).
-
-Read more on FFTW usage on the [FFTW
-website.](http://www.fftw.org/fftw3_doc/)
-
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-GSL
-===
-
-The GNU Scientific Library. Provides a wide range of mathematical
-routines.
-
-
-
-Introduction
-------------
-
-The GNU Scientific Library (GSL) provides a wide range of mathematical
-routines such as random number generators, special functions and
-least-squares fitting. There are over 1000 functions in total. The
-routines have been written from scratch in C, and present a modern
-Applications Programming Interface (API) for C programmers, allowing
-wrappers to be written for very high level languages.
-
-The library covers a wide range of topics in numerical computing.
-Routines are available for the following areas:
-
- ------------------ |---|---|-------------- ------------------------
- Complex Numbers Roots of Polynomials
-
- Special Functions Vectors and Matrices
-
- Permutations Combinations
-
- Sorting BLAS Support
-
- Linear Algebra CBLAS Library
-
- Fast Fourier Transforms Eigensystems
-
- Random Numbers Quadrature
-
- Random Distributions Quasi-Random Sequences
-
- Histograms Statistics
-
- Monte Carlo Integration N-Tuples
-
- Differential Equations Simulated Annealing
-
- Numerical Interpolation
- Differentiation
-
- Series Acceleration Chebyshev Approximations
-
- Root-Finding Discrete Hankel
- Transforms
-
- Least-Squares Fitting Minimization
-
- IEEE Floating-Point Physical Constants
-
- Basis Splines Wavelets
- ------------------ |---|---|-------------- ------------------------
-
-Modules
--------
-
-The GSL 1.16 is available on Anselm, compiled for GNU and Intel
-compiler. These variants are available via modules:
-
- Module Compiler
- ----------------- |---|---|-
- gsl/1.16-gcc gcc 4.8.6
- gsl/1.16-icc(default) icc
-
- Â $ module load gsl
-
-The module sets up environment variables, required for linking and
-running GSL enabled applications. This particular command loads the
-default module, which is gsl/1.16-icc
-
-Linking
--------
-
-Load an appropriate gsl module. Link using **-lgsl** switch to link your
-code against GSL. The GSL depends on cblas API to BLAS library, which
-must be supplied for linking. The BLAS may be provided, for example from
-the MKL library, as well as from the BLAS GSL library (-lgslcblas).
-Using the MKL is recommended.
-
-### Compiling and linking with Intel compilers
-
- $ module load intel
- $ module load gsl
- $ icc myprog.c -o myprog.x -Wl,-rpath=$LIBRARY_PATH -mkl -lgsl
-
-### Compiling and linking with GNU compilers
-
- $ module load gcc
- $ module load mkl
- $ module load gsl/1.16-gcc
- $ gcc myprog.c -o myprog.x -Wl,-rpath=$LIBRARY_PATH -lmkl_intel_lp64 -lmkl_gnu_thread -lmkl_core -lgomp -lgsl
-
-Example
--------
-
-Following is an example of discrete wavelet transform implemented by
-GSL:
-
- #include <stdio.h>
- #include <math.h>
- #include <gsl/gsl_sort.h>
- #include <gsl/gsl_wavelet.h>
-
- int
- main (int argc, char **argv)
- {
- Â int i, n = 256, nc = 20;
- Â double *data = malloc (n * sizeof (double));
- Â double *abscoeff = malloc (n * sizeof (double));
- Â size_t *p = malloc (n * sizeof (size_t));
-
- Â gsl_wavelet *w;
- Â gsl_wavelet_workspace *work;
-
- Â w = gsl_wavelet_alloc (gsl_wavelet_daubechies, 4);
- Â work = gsl_wavelet_workspace_alloc (n);
-
- Â for (i=0; i<n; i++)
- Â data[i] = sin (3.141592654*(double)i/256.0);
-
- Â gsl_wavelet_transform_forward (w, data, 1, n, work);
-
- Â for (i = 0; i < n; i++)
- Â Â Â {
- Â Â Â Â Â abscoeff[i] = fabs (data[i]);
- Â Â Â }
- Â
- Â gsl_sort_index (p, abscoeff, 1, n);
- Â
- Â for (i = 0; (i + nc) < n; i++)
- Â Â Â data[p[i]] = 0;
- Â
- Â gsl_wavelet_transform_inverse (w, data, 1, n, work);
- Â
- Â for (i = 0; i < n; i++)
- Â Â Â {
- Â Â Â Â Â printf ("%gn", data[i]);
- Â Â Â }
- Â
- Â gsl_wavelet_free (w);
- Â gsl_wavelet_workspace_free (work);
-
- Â free (data);
- Â free (abscoeff);
- Â free (p);
- Â return 0;
- }
-
-Load modules and compile:
-
- $ module load intel gsl
- icc dwt.c -o dwt.x -Wl,-rpath=$LIBRARY_PATH -mkl -lgsl
-
-In this example, we compile the dwt.c code using the Intel compiler and
-link it to the MKL and GSL library, note the -mkl and -lgsl options. The
-library search path is compiled in, so that no modules are necessary to
-run the code.
-
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-HDF5
-====
-
-Hierarchical Data Format library. Serial and MPI parallel version.
-
-Â
-
-[HDF5 (Hierarchical Data Format)](http://www.hdfgroup.org/HDF5/) is a
-general purpose library and file format for storing scientific data.
-HDF5 can store two primary objects: datasets and groups. A dataset is
-essentially a multidimensional array of data elements, and a group is a
-structure for organizing objects in an HDF5 file. Using these two basic
-objects, one can create and store almost any kind of scientific data
-structure, such as images, arrays of vectors, and structured and
-unstructured grids. You can also mix and match them in HDF5 files
-according to your needs.
-
-Versions **1.8.11** and **1.8.13** of HDF5 library are available on
-Anselm, compiled for **Intel MPI** and **OpenMPI** using **intel** and
-gnu** compilers. These are available via modules:
-
- |Version |Parallelization |module |C linker options<th align="left">C++ linker options<th align="left">Fortran linker options |
- | --- | --- |
- |HDF5 icc serial |pthread |hdf5/1.8.11 |$HDF5_INC $HDF5_SHLIB |$HDF5_INC $HDF5_CPP_LIB |$HDF5_INC $HDF5_F90_LIB |
- |HDF5 icc parallel MPI\ |pthread, IntelMPI |hdf5-parallel/1.8.11 |$HDF5_INC $HDF5_SHLIB |Not supported |$HDF5_INC $HDF5_F90_LIB |
- |HDF5 icc serial |pthread |hdf5/1.8.13 |$HDF5_INC $HDF5_SHLIB |$HDF5_INC $HDF5_CPP_LIB |$HDF5_INC $HDF5_F90_LIB |
- |HDF5 icc parallel MPI\ |pthread, IntelMPI |hdf5-parallel/1.8.13 |$HDF5_INC $HDF5_SHLIB |Not supported |$HDF5_INC $HDF5_F90_LIB |
- |HDF5 gcc parallel MPI\ |pthread, OpenMPI 1.6.5, gcc 4.8.1 |hdf5-parallel/1.8.11-gcc |$HDF5_INC $HDF5_SHLIB |Not supported |$HDF5_INC $HDF5_F90_LIB |
- |HDF5 gcc parallel MPI\ |pthread, OpenMPI 1.6.5, gcc 4.8.1 |hdf5-parallel/1.8.13-gcc |$HDF5_INC $HDF5_SHLIB |Not supported |$HDF5_INC $HDF5_F90_LIB |
- |HDF5 gcc parallel MPI\ |pthread, OpenMPI 1.8.1, gcc 4.9.0 |hdf5-parallel/1.8.13-gcc49 |$HDF5_INC $HDF5_SHLIB |Not supported |$HDF5_INC $HDF5_F90_LIB |
-
-Â
-
- $ module load hdf5-parallel
-
-The module sets up environment variables, required for linking and
-running HDF5 enabled applications. Make sure that the choice of HDF5
-module is consistent with your choice of MPI library. Mixing MPI of
-different implementations may have unpredictable results.
-
-Be aware, that GCC version of **HDF5 1.8.11** has serious performance
-issues, since it's compiled with -O0 optimization flag. This version is
-provided only for testing of code compiled only by GCC and IS NOT
-recommended for production computations. For more informations, please
-see:
-<http://www.hdfgroup.org/ftp/HDF5/prev-releases/ReleaseFiles/release5-1811>
-All GCC versions of **HDF5 1.8.13** are not affected by the bug, are
-compiled with -O3 optimizations and are recommended for production
-computations.
-
-Example
--------
-
- #include "hdf5.h"
- #define FILE "dset.h5"
-
- int main() {
-
- hid_t file_id, dataset_id, dataspace_id; /* identifiers */
- hsize_t dims[2];
- herr_t status;
- int i, j, dset_data[4][6];
-
- /* Create a new file using default properties. */
- file_id = H5Fcreate(FILE, H5F_ACC_TRUNC, H5P_DEFAULT, H5P_DEFAULT);
-
- /* Create the data space for the dataset. */
- dims[0] = 4;
- dims[1] = 6;
- dataspace_id = H5Screate_simple(2, dims, NULL);
-
- /* Initialize the dataset. */
- for (i = 0; i < 4; i++)
- for (j = 0; j < 6; j++)
- dset_data[i][j] = i * 6 + j + 1;
-
- /* Create the dataset. */
- dataset_id = H5Dcreate2(file_id, "/dset", H5T_STD_I32BE, dataspace_id,
- H5P_DEFAULT, H5P_DEFAULT, H5P_DEFAULT);
-
- /* Write the dataset. */
- status = H5Dwrite(dataset_id, H5T_NATIVE_INT, H5S_ALL, H5S_ALL, H5P_DEFAULT,
- dset_data);
-
- status = H5Dread(dataset_id, H5T_NATIVE_INT, H5S_ALL, H5S_ALL, H5P_DEFAULT,
- dset_data);
-
- /* End access to the dataset and release resources used by it. */
- status = H5Dclose(dataset_id);
-
- /* Terminate access to the data space. */
- status = H5Sclose(dataspace_id);
-
- /* Close the file. */
- status = H5Fclose(file_id);
- }
-
-Load modules and compile:
-
- $ module load intel impi
- $ module load hdf5-parallel
-
- $ mpicc hdf5test.c -o hdf5test.x -Wl,-rpath=$LIBRARY_PATH $HDF5_INC $HDF5_SHLIB
-
- Run the example as [Intel MPI
-program](../anselm-cluster-documentation/software/mpi-1/running-mpich2.html).
-
-For further informations, please see the website:
-<http://www.hdfgroup.org/HDF5/>
-
-Â
-
-Â
-
-Â
-
-class="smarterwiki-popup-bubble-tip">
-
-btnI=I'm+Feeling+Lucky&btnI=I'm+Feeling+Lucky&q=HDF5%20icc%20serial%09pthread%09hdf5%2F1.8.13%09%24HDF5_INC%20%24HDF5_SHLIB%09%24HDF5_INC%20%24HDF5_CPP_LIB%09%24HDF5_INC%20%24HDF5_F90_LIB%0A%0AHDF5%20icc%20parallel%20MPI%0A%09pthread%2C%20IntelMPI%09hdf5-parallel%2F1.8.13%09%24HDF5_INC%20%24HDF5_SHLIB%09Not%20supported%09%24HDF5_INC%20%24HDF5_F90_LIB+wikipedia "Search Wikipedia"){.smarterwiki-popup-bubble
-
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--- a/converted/docs.it4i.cz/anselm-cluster-documentation/software/numerical-libraries/intel-numerical-libraries.md
+++ /dev/null
@@ -1,54 +0,0 @@
-Intel numerical libraries
-=========================
-
-Intel libraries for high performance in numerical computing
-
-
-
-Intel Math Kernel Library
--------------------------
-
-Intel Math Kernel Library (Intel MKL) is a library of math kernel
-subroutines, extensively threaded and optimized for maximum performance.
-Intel MKL unites and provides these basic components: BLAS, LAPACK,
-ScaLapack, PARDISO, FFT, VML, VSL, Data fitting, Feast Eigensolver and
-many more.
-
- $ module load mkl
-
-Read more at the [Intel
-MKL](../intel-suite/intel-mkl.html) page.
-
-Intel Integrated Performance Primitives
----------------------------------------
-
-Intel Integrated Performance Primitives, version 7.1.1, compiled for AVX
-is available, via module ipp. The IPP is a library of highly optimized
-algorithmic building blocks for media and data applications. This
-includes signal, image and frame processing algorithms, such as FFT,
-FIR, Convolution, Optical Flow, Hough transform, Sum, MinMax and many
-more.
-
- $ module load ipp
-
-Read more at the [Intel
-IPP](../intel-suite/intel-integrated-performance-primitives.html)
-page.
-
-Intel Threading Building Blocks
--------------------------------
-
-Intel Threading Building Blocks (Intel TBB) is a library that supports
-scalable parallel programming using standard ISO C++ code. It does not
-require special languages or compilers. It is designed to promote
-scalable data parallel programming. Additionally, it fully supports
-nested parallelism, so you can build larger parallel components from
-smaller parallel components. To use the library, you specify tasks, not
-threads, and let the library map tasks onto threads in an efficient
-manner.
-
- $ module load tbb
-
-Read more at the [Intel
-TBB](../intel-suite/intel-tbb.html) page.
-
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--- a/converted/docs.it4i.cz/anselm-cluster-documentation/software/numerical-libraries/magma-for-intel-xeon-phi.md
+++ /dev/null
@@ -1,93 +0,0 @@
-MAGMA for Intel Xeon Phi
-========================
-
-Next generation dense algebra library for heterogeneous systems with
-accelerators
-
-### Compiling and linking with MAGMA
-
-To be able to compile and link code with MAGMA library user has to load
-following module:
-
- $ module load magma/1.3.0-mic
-
-To make compilation more user friendly module also sets these two
-environment variables:
-
-MAGMA_INC - contains paths to the MAGMA header files (to be used for
-compilation step)
-
-MAGMA_LIBS - contains paths to MAGMA libraries (to be used for linking
-step). Â
-
-Compilation example:
-
- $ icc -mkl -O3 -DHAVE_MIC -DADD_ -Wall $MAGMA_INC -c testing_dgetrf_mic.cpp -o testing_dgetrf_mic.o
-
- $ icc -mkl -O3 -DHAVE_MIC -DADD_ -Wall -fPIC -Xlinker -zmuldefs -Wall -DNOCHANGE -DHOST testing_dgetrf_mic.o -o testing_dgetrf_mic $MAGMA_LIBS
-
-Â
-
-### Running MAGMA code
-
-MAGMA implementation for Intel MIC requires a MAGMA server running on
-accelerator prior to executing the user application. The server can be
-started and stopped using following scripts:
-
-To start MAGMA server use:
-$MAGMAROOT/start_magma_server**
-
-To stop the server use:
-$MAGMAROOT/stop_magma_server**
-
-For deeper understanding how the MAGMA server is started, see the
-following script:
-$MAGMAROOT/launch_anselm_from_mic.sh**
-
-To test if the MAGMA server runs properly we can run one of examples
-that are part of the MAGMA installation:
-
- [user@cn204 ~]$ $MAGMAROOT/testing/testing_dgetrf_mic
-
- [user@cn204 ~]$ export OMP_NUM_THREADS=16
-
- [lriha@cn204 ~]$ $MAGMAROOT/testing/testing_dgetrf_mic
- Usage: /apps/libs/magma-mic/magmamic-1.3.0/testing/testing_dgetrf_mic [options] [-h|--help]
-
- Â MÂ Â Â Â NÂ Â Â Â CPU GFlop/s (sec)Â Â MAGMA GFlop/s (sec)Â Â ||PA-LU||/(||A||*N)
- =========================================================================
- Â 1088Â 1088Â Â Â Â ---Â Â (Â ---Â )Â Â Â Â 13.93 (Â Â 0.06)Â Â Â Â ---
- Â 2112Â 2112Â Â Â Â ---Â Â (Â ---Â )Â Â Â Â 77.85 (Â Â 0.08)Â Â Â Â ---
- Â 3136Â 3136Â Â Â Â ---Â Â (Â ---Â )Â Â Â 183.21 (Â Â 0.11)Â Â Â Â ---
- Â 4160Â 4160Â Â Â Â ---Â Â (Â ---Â )Â Â Â 227.52 (Â Â 0.21)Â Â Â Â ---
- Â 5184Â 5184Â Â Â Â ---Â Â (Â ---Â )Â Â Â 258.61 (Â Â 0.36)Â Â Â Â ---
- Â 6208Â 6208Â Â Â Â ---Â Â (Â ---Â )Â Â Â 333.12 (Â Â 0.48)Â Â Â Â ---
- Â 7232Â 7232Â Â Â Â ---Â Â (Â ---Â )Â Â Â 416.52 (Â Â 0.61)Â Â Â Â ---
- Â 8256Â 8256Â Â Â Â ---Â Â (Â ---Â )Â Â Â 446.97 (Â Â 0.84)Â Â Â Â ---
- Â 9280Â 9280Â Â Â Â ---Â Â (Â ---Â )Â Â Â 461.15 (Â Â 1.16)Â Â Â Â ---
- 10304 10304Â Â Â Â ---Â Â (Â ---Â )Â Â Â 500.70 (Â Â 1.46)Â Â Â Â ---
-
-Â
-
-Please note: MAGMA contains several benchmarks and examples that can be
-found in:
-$MAGMAROOT/testing/**
-
-MAGMA relies on the performance of all CPU cores as well as on the
-performance of the accelerator. Therefore on Anselm number of CPU OpenMP
-threads has to be set to 16: Â **
-export OMP_NUM_THREADS=16**
-
-Â
-
-See more details at [MAGMA home
-page](http://icl.cs.utk.edu/magma/).
-
-References
-----------
-
-[1] MAGMA MIC: Linear Algebra Library for Intel Xeon Phi Coprocessors,
-Jack Dongarra et. al,Â
-[http://icl.utk.edu/projectsfiles/magma/pubs/24-MAGMA_MIC_03.pdf
-](http://icl.utk.edu/projectsfiles/magma/pubs/24-MAGMA_MIC_03.pdf)
-
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-PETSc
-=====
-
-PETSc is a suite of building blocks for the scalable solution of
-scientific and engineering applications modelled by partial differential
-equations. It supports MPI, shared memory, and GPUs through CUDA or
-OpenCL, as well as hybrid MPI-shared memory or MPI-GPU parallelism.
-
-
-
-Introduction
-------------
-
-PETSc (Portable, Extensible Toolkit for Scientific Computation) is a
-suite of building blocks (data structures and routines) for the scalable
-solution of scientific and engineering applications modelled by partial
-differential equations. It allows thinking in terms of high-level
-objects (matrices) instead of low-level objects (raw arrays). Written in
-C language but can also be called from FORTRAN, C++, Python and Java
-codes. It supports MPI, shared memory, and GPUs through CUDA or OpenCL,
-as well as hybrid MPI-shared memory or MPI-GPU parallelism.
-
-Resources
----------
-
-- [project webpage](http://www.mcs.anl.gov/petsc/)
-- [documentation](http://www.mcs.anl.gov/petsc/documentation/)
- - [PETSc Users
- Manual (PDF)](http://www.mcs.anl.gov/petsc/petsc-current/docs/manual.pdf)
- - [index of all manual
- pages](http://www.mcs.anl.gov/petsc/petsc-current/docs/manualpages/singleindex.html)
-- PRACE Video Tutorial [part
- 1](http://www.youtube.com/watch?v=asVaFg1NDqY), [part
- 2](http://www.youtube.com/watch?v=ubp_cSibb9I), [part
- 3](http://www.youtube.com/watch?v=vJAAAQv-aaw), [part
- 4](http://www.youtube.com/watch?v=BKVlqWNh8jY), [part
- 5](http://www.youtube.com/watch?v=iXkbLEBFjlM)
-
-Modules
--------
-
-You can start using PETSc on Anselm by loading the PETSc module. Module
-names obey this pattern:
-
- # module load petsc/version-compiler-mpi-blas-variant, e.g.
- module load petsc/3.4.4-icc-impi-mkl-opt
-
-where `variant` is replaced by one of
-`{dbg, opt, threads-dbg, threads-opt}`. The `opt` variant is compiled
-without debugging information (no `-g` option) and with aggressive
-compiler optimizations (`-O3 -xAVX`). This variant is suitable for
-performance measurements and production runs. In all other cases use the
-debug (`dbg`) variant, because it contains debugging information,
-performs validations and self-checks, and provides a clear stack trace
-and message in case of an error. The other two variants `threads-dbg`
-and `threads-opt` are `dbg` and `opt`, respectively, built with [OpenMP
-and pthreads threading
-support](http://www.mcs.anl.gov/petsc/features/threads.html).
-
-External libraries
-------------------
-
-PETSc needs at least MPI, BLAS and LAPACK. These dependencies are
-currently satisfied with Intel MPI and Intel MKL in Anselm `petsc`
-modules.
-
-PETSc can be linked with a plethora of [external numerical
-libraries](http://www.mcs.anl.gov/petsc/miscellaneous/external.html),
-extending PETSc functionality, e.g. direct linear system solvers,
-preconditioners or partitioners. See below a list of libraries currently
-included in Anselm `petsc` modules.
-
-All these libraries can be used also alone, without PETSc. Their static
-or shared program libraries are available in
-`$PETSC_DIR/$PETSC_ARCH/lib` and header files in
-`$PETSC_DIR/$PETSC_ARCH/include`. `PETSC_DIR` and `PETSC_ARCH` are
-environment variables pointing to a specific PETSc instance based on the
-petsc module loaded.
-
-### Libraries linked to PETSc on Anselm (as of 11 April 2015)
-
-- dense linear algebra
- - [Elemental](http://libelemental.org/)
-- sparse linear system solvers
- - [Intel MKL
- Pardiso](https://software.intel.com/en-us/node/470282)
- - [MUMPS](http://mumps.enseeiht.fr/)
- - [PaStiX](http://pastix.gforge.inria.fr/)
- - [SuiteSparse](http://faculty.cse.tamu.edu/davis/suitesparse.html)
- - [SuperLU](http://crd.lbl.gov/~xiaoye/SuperLU/#superlu)
- - [SuperLU_Dist](http://crd.lbl.gov/~xiaoye/SuperLU/#superlu_dist)
-- input/output
- - [ExodusII](http://sourceforge.net/projects/exodusii/)
- - [HDF5](http://www.hdfgroup.org/HDF5/)
- - [NetCDF](http://www.unidata.ucar.edu/software/netcdf/)
-- partitioning
- - [Chaco](http://www.cs.sandia.gov/CRF/chac.html)
- - [METIS](http://glaros.dtc.umn.edu/gkhome/metis/metis/overview)
- - [ParMETIS](http://glaros.dtc.umn.edu/gkhome/metis/parmetis/overview)
- - [PT-Scotch](http://www.labri.fr/perso/pelegrin/scotch/)
-- preconditioners & multigrid
- - [Hypre](http://acts.nersc.gov/hypre/)
- - [Trilinos ML](http://trilinos.sandia.gov/packages/ml/)
- - [SPAI - Sparse Approximate
- Inverse](https://bitbucket.org/petsc/pkg-spai)
-
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-Trilinos
-========
-
-Packages for large scale scientific and engineering problems. Provides
-MPI and hybrid parallelization.
-
-### Introduction
-
-Trilinos is a collection of software packages for the numerical solution
-of large scale scientific and engineering problems. It is based on C++
-and feautures modern object-oriented design. Both serial as well as
-parallel computations based on MPI and hybrid parallelization are
-supported within Trilinos packages.
-
-### Installed packages
-
-Current Trilinos installation on ANSELM contains (among others) the
-following main packages
-
-- **Epetra** - core linear algebra package containing classes for
- manipulation with serial and distributed vectors, matrices,
- and graphs. Dense linear solvers are supported via interface to BLAS
- and LAPACK (Intel MKL on ANSELM). Its extension **EpetraExt**
- contains e.g. methods for matrix-matrix multiplication.
-- **Tpetra** - next-generation linear algebra package. Supports 64bit
- indexing and arbitrary data type using C++ templates.
-- **Belos** - library of various iterative solvers (CG, block CG,
- GMRES, block GMRES etc.).
-- **Amesos** - interface to direct sparse solvers.
-- **Anasazi** - framework for large-scale eigenvalue algorithms.
-- **IFPACK** - distributed algebraic preconditioner (includes e.g.
- incomplete LU factorization)
-- **Teuchos** - common tools packages. This package contains classes
- for memory management, output, performance monitoring, BLAS and
- LAPACK wrappers etc.
-
-For the full list of Trilinos packages, descriptions of their
-capabilities, and user manuals see
-[http://trilinos.sandia.gov.](http://trilinos.sandia.gov)
-
-### Installed version
-
-Currently, Trilinos in version 11.2.3 compiled with Intel Compiler is
-installed on ANSELM.
-
-### Compilling against Trilinos
-
-First, load the appropriate module:
-
- $ module load trilinos
-
-For the compilation of CMake-aware project, Trilinos provides the
-FIND_PACKAGE( Trilinos ) capability, which makes it easy to build
-against Trilinos, including linking against the correct list of
-libraries. For details, see
-<http://trilinos.sandia.gov/Finding_Trilinos.txt>
-
-For compiling using simple makefiles, Trilinos provides Makefile.export
-system, which allows users to include important Trilinos variables
-directly into their makefiles. This can be done simply by inserting the
-following line into the makefile:
-
- include Makefile.export.Trilinos
-
-or
-
- include Makefile.export.<package>
-
-if you are interested only in a specific Trilinos package. This will
-give you access to the variables such as Trilinos_CXX_COMPILER,
-Trilinos_INCLUDE_DIRS, Trilinos_LIBRARY_DIRS etc. For the detailed
-description and example makefile see
-<http://trilinos.sandia.gov/Export_Makefile.txt>.
-
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-nVidia CUDA
-===========
-
-A guide to nVidia CUDA programming and GPU usage
-
-
-
-CUDA Programming on Anselm
---------------------------
-
-The default programming model for GPU accelerators on Anselm is Nvidia
-CUDA. To set up the environment for CUDA use
-
- $ module load cuda
-
-If the user code is hybrid and uses both CUDA and MPI, the MPI
-environment has to be set up as well. One way to do this is to use
-the PrgEnv-gnu module, which sets up correct combination of GNU compiler
-and MPI library.
-
- $ module load PrgEnv-gnu
-
-CUDA code can be compiled directly on login1 or login2 nodes. User does
-not have to use compute nodes with GPU accelerator for compilation. To
-compile a CUDA source code, use nvcc compiler.
-
- $ nvcc --version
-
-CUDA Toolkit comes with large number of examples, that can be
-helpful to start with. To compile and test these examples user should
-copy them to its home directoryÂ
-
- $ cd ~
- $ mkdir cuda-samples
- $ cp -R /apps/nvidia/cuda/6.5.14/samples/* ~/cuda-samples/
-
-To compile an examples, change directory to the particular example (here
-the example used is deviceQuery) and run "make" to start the compilation
-
- $ cd ~/cuda-samples/1_Utilities/deviceQuery
- $ makeÂ
-
-To run the code user can use PBS interactive session to get access to a
-node from qnvidia queue (note: use your project name with parameter -A
-in the qsub command) and execute the binary file
-
- $ qsub -I -q qnvidia -A OPEN-0-0
- $ module load cuda
- $Â ~/cuda-samples/1_Utilities/deviceQuery/deviceQuery
-
-Expected output of the deviceQuery example executed on a node with Tesla
-K20m is
-
- CUDA Device Query (Runtime API) version (CUDART static linking)
-
- Detected 1 CUDA Capable device(s)
-
- Device 0: "Tesla K20m"
- CUDA Driver Version / Runtime Version 5.0 / 5.0
- CUDA Capability Major/Minor version number: 3.5
- Total amount of global memory: 4800 MBytes (5032706048 bytes)
- (13) Multiprocessors x (192) CUDA Cores/MP: 2496 CUDA Cores
- GPU Clock rate: 706 MHz (0.71 GHz)
- Memory Clock rate: 2600 MhzÂ
- Memory Bus Width: 320-bit
- L2 Cache Size: 1310720 bytes
- Max Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536,65536), 3D=(4096,4096,4096)
- Max Layered Texture Size (dim) x layers 1D=(16384) x 2048, 2D=(16384,16384) x 2048
- Total amount of constant memory: 65536 bytesÂ
- Total amount of shared memory per block: 49152 bytes
- Total number of registers available per block: 65536
- Warp size: 32
- Maximum number of threads per multiprocessor: 2048
- Maximum number of threads per block: 1024Â
- Maximum sizes of each dimension of a block: 1024 x 1024 x 64
- Maximum sizes of each dimension of a grid: 2147483647 x 65535 x 65535
- Maximum memory pitch: 2147483647 bytes
- Texture alignment: 512 bytes
- Concurrent copy and kernel execution: Yes with 2 copy engine(s)
- Run time limit on kernels: No
- Integrated GPU sharing Host Memory: No
- Support host page-locked memory mapping: Yes
- Alignment requirement for Surfaces: YesÂ
- Device has ECC support: EnabledÂ
- Device supports Unified Addressing (UVA): Yes
- Device PCI Bus ID / PCI location ID: 2 / 0
- Compute Mode:Â
- < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >Â
- deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 5.0, CUDA Runtime Version = 5.0, NumDevs = 1, Device0 = Tesla K20mÂ
-
-### Code example
-
-In this section we provide a basic CUDA based vector addition code
-example. You can directly copy and paste the code to test it.Â
-
- $ vim test.cu
-
- #define N (2048*2048)
- #define THREADS_PER_BLOCK 512
-
- #include <stdio.h>
- #include <stdlib.h>
-
- // GPU kernel function to add two vectors
- __global__ void add_gpu( int *a, int *b, int *c, int n){
- Â int index = threadIdx.x + blockIdx.x * blockDim.x;
- Â if (index < n)
- Â c[index] = a[index] + b[index];
- }
-
- // CPU function to add two vectors
- void add_cpu (int *a, int *b, int *c, int n) {
- Â for (int i=0; i < n; i++)
- c[i] = a[i] + b[i];
- }
-
- // CPU function to generate a vector of random integers
- void random_ints (int *a, int n) {
- Â for (int i = 0; i < n; i++)
- Â a[i] = rand() % 10000; // random number between 0 and 9999
- }
-
- // CPU function to compare two vectors
- int compare_ints( int *a, int *b, int n ){
- Â int pass = 0;
- Â for (int i = 0; i < N; i++){
- Â if (a[i] != b[i]) {
- Â printf("Value mismatch at location %d, values %d and %dn",i, a[i], b[i]);
- Â pass = 1;
- Â }
- Â }
- Â if (pass == 0) printf ("Test passedn"); else printf ("Test Failedn");
- Â return pass;
- }
-
- int main( void ) {
- Â
- Â int *a, *b, *c; // host copies of a, b, c
- Â int *dev_a, *dev_b, *dev_c; // device copies of a, b, c
- Â int size = N * sizeof( int ); // we need space for N integers
-
- Â // Allocate GPU/device copies of dev_a, dev_b, dev_c
- Â cudaMalloc( (void**)&dev_a, size );
- Â cudaMalloc( (void**)&dev_b, size );
- Â cudaMalloc( (void**)&dev_c, size );
-
- Â // Allocate CPU/host copies of a, b, c
- Â a = (int*)malloc( size );
- Â b = (int*)malloc( size );
- Â c = (int*)malloc( size );
- Â
- Â // Fill input vectors with random integer numbers
- Â random_ints( a, N );
- Â random_ints( b, N );
-
- Â // copy inputs to device
- Â cudaMemcpy( dev_a, a, size, cudaMemcpyHostToDevice );
- Â cudaMemcpy( dev_b, b, size, cudaMemcpyHostToDevice );
-
- Â // launch add_gpu() kernel with blocks and threads
- Â add_gpu<<< N/THREADS_PER_BLOCK, THREADS_PER_BLOCK >>( dev_a, dev_b, dev_c, N );
-
- Â // copy device result back to host copy of c
- Â cudaMemcpy( c, dev_c, size, cudaMemcpyDeviceToHost );
-
- Â //Check the results with CPU implementation
- Â int *c_h; c_h = (int*)malloc( size );
- Â add_cpu (a, b, c_h, N);
- Â compare_ints(c, c_h, N);
-
- Â // Clean CPU memory allocations
- Â free( a ); free( b ); free( c ); free (c_h);
-
- Â // Clean GPU memory allocations
- Â cudaFree( dev_a );
- Â cudaFree( dev_b );
- Â cudaFree( dev_c );
-
- Â return 0;
- }
-
-This code can be compiled using following command
-
- $ nvcc test.cu -o test_cuda
-
-To run the code use interactive PBS session to get access to one of the
-GPU accelerated nodes
-
- $ qsub -I -q qnvidia -A OPEN-0-0
- $ module load cuda
- $Â ./test.cuda
-
-CUDA Libraries
---------------
-
-### CuBLAS
-
-The NVIDIA CUDA Basic Linear Algebra Subroutines (cuBLAS) library is a
-GPU-accelerated version of the complete standard BLAS library with 152
-standard BLAS routines. Basic description of the library together with
-basic performance comparison with MKL can be found
-[here](https://developer.nvidia.com/cublas "Nvidia cuBLAS").
-
-CuBLAS example: SAXPY**
-
-SAXPY function multiplies the vector x by the scalar alpha and adds it
-to the vector y overwriting the latest vector with the result. The
-description of the cuBLAS function can be found in [NVIDIA CUDA
-documentation](http://docs.nvidia.com/cuda/cublas/index.html#cublas-lt-t-gt-axpy "Nvidia CUDA documentation ").
-Code can be pasted in the file and compiled without any modification.
-
- /* Includes, system */
- #include <stdio.h>
- #include <stdlib.h>
-
- /* Includes, cuda */
- #include <cuda_runtime.h>
- #include <cublas_v2.h>
-
- /* Vector size */
- #define NÂ (32)
-
- /* Host implementation of a simple version of saxpi */
- void saxpy(int n, float alpha, const float *x, float *y)
- {
- Â Â Â for (int i = 0; i < n; ++i)
- Â Â Â y[i] = alpha*x[i] + y[i];
- }
-
- /* Main */
- int main(int argc, char **argv)
- {
- Â Â Â float *h_X, *h_Y, *h_Y_ref;
- Â Â Â float *d_X = 0;
- Â Â Â float *d_Y = 0;
-
- Â Â Â const float alpha = 1.0f;
- Â Â Â int i;
-
- Â Â Â cublasHandle_t handle;
-
- Â Â Â /* Initialize CUBLAS */
- Â Â Â printf("simpleCUBLAS test running..n");
- Â Â Â cublasCreate(&handle);
-
- Â Â Â /* Allocate host memory for the matrices */
- Â Â Â h_X = (float *)malloc(N * sizeof(h_X[0]));
- Â Â Â h_Y = (float *)malloc(N * sizeof(h_Y[0]));
- Â Â Â h_Y_ref = (float *)malloc(N * sizeof(h_Y_ref[0]));
-
- Â Â Â /* Fill the matrices with test data */
- Â Â Â for (i = 0; i < N; i++)
- Â Â Â {
- Â Â Â Â Â Â Â h_X[i] = rand() / (float)RAND_MAX;
- Â Â Â Â Â Â Â h_Y[i] = rand() / (float)RAND_MAX;
- Â Â Â Â Â Â Â h_Y_ref[i] = h_Y[i];
- Â Â Â }
-
- Â Â Â /* Allocate device memory for the matrices */
- Â Â Â cudaMalloc((void **)&d_X, N * sizeof(d_X[0]));
- Â Â Â cudaMalloc((void **)&d_Y, N * sizeof(d_Y[0]));
-
- Â Â Â /* Initialize the device matrices with the host matrices */
- Â Â Â cublasSetVector(N, sizeof(h_X[0]), h_X, 1, d_X, 1);
- Â Â Â cublasSetVector(N, sizeof(h_Y[0]), h_Y, 1, d_Y, 1);
-
- Â Â Â /* Performs operation using plain C code */
- Â Â Â saxpy(N, alpha, h_X, h_Y_ref);
-
- Â Â Â /* Performs operation using cublas */
- Â Â Â cublasSaxpy(handle, N, &alpha, d_X, 1, d_Y, 1);
-
- Â Â Â /* Read the result back */
- Â Â Â cublasGetVector(N, sizeof(h_Y[0]), d_Y, 1, h_Y, 1);
-
- Â Â Â /* Check result against reference */
- Â Â Â for (i = 0; i < N; ++i)
- Â Â Â Â Â Â Â printf("CPU res = %f t GPU res = %f t diff = %f n", h_Y_ref[i], h_Y[i], h_Y_ref[i] - h_Y[i]);
-
- Â Â Â /* Memory clean up */
- Â Â Â free(h_X); free(h_Y); free(h_Y_ref);
- Â Â Â cudaFree(d_X); cudaFree(d_Y);
-
- Â Â Â /* Shutdown */
- Â Â Â cublasDestroy(handle);
- }
-
-Â Please note: cuBLAS has its own function for data transfers between CPU
-and GPU memory:
-Â -
-[cublasSetVector](http://docs.nvidia.com/cuda/cublas/index.html#cublassetvector)
-- transfers data from CPU to GPU memory
-Â -
-[cublasGetVector](http://docs.nvidia.com/cuda/cublas/index.html#cublasgetvector)
-- transfers data from GPU to CPU memory
-
-Â To compile the code using NVCC compiler a "-lcublas" compiler flag has
-to be specified:
-
- $ module load cuda
- $ nvcc -lcublas test_cublas.cu -o test_cublas_nvcc
-
-To compile the same code with GCC:
-
- $ module load cuda
- $ gcc -std=c99 test_cublas.c -o test_cublas_icc -lcublas -lcudart
-
-To compile the same code with Intel compiler:
-
- $ module load cuda intel
- $ icc -std=c99 test_cublas.c -o test_cublas_icc -lcublas -lcudart
-
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-Diagnostic component (TEAM)
-===========================
-
-
-
-### Access
-
-TEAM is available at the following address
-:Â <http://omics.it4i.cz/team/>
-
-The address is accessible only via
-[VPN. ](../../accessing-the-cluster/vpn-access.html)
-
-### Diagnostic component (TEAM) {#diagnostic-component-team}
-
-VCF files are scanned by this diagnostic tool for known diagnostic
-disease-associated variants. When no diagnostic mutation is found, the
-file can be sent to the disease-causing gene discovery tool to see
-wheter new disease associated variants can be found.
-
-TEAMÂ >(27)Â is an intuitive and easy-to-use web tool that
-fills the gap between the predicted mutations and the final diagnostic
-in targeted enrichment sequencing analysis. The tool searches for known
-diagnostic mutations, corresponding to a disease panel, among the
-predicted patient’s variants. Diagnostic variants for the disease are
-taken from four databases of disease-related variants (HGMD-public,
-HUMSAVAR , ClinVar and COSMIC) If no primary diagnostic variant is
-found, then a list of secondary findings that can help to establish a
-diagnostic is produced. TEAM also provides with an interface for the
-definition of and customization of panels, by means of which, genes and
-mutations can be added or discarded to adjust panel definitions.Â
-
-
-
-Â
-
-*Figure 5. ***Interface of the application. Panels for defining
-targeted regions of interest can be set up by just drag and drop known
-disease genes or disease definitions from the lists. Thus, virtual
-panels can be interactively improved as the knowledge of the disease
-increases.*
-
-*
-*
-
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-Overview
-========
-
-The human NGS data processing solution
-
-
-
-Introduction
-------------
-
-The scope of this OMICS MASTER solution is restricted to human genomics
-research (disease causing gene discovery in whole human genome or exome)
-or diagnosis (panel sequencing), although it could be extended in the
-future to other usages.
-
-The pipeline inputs the raw data produced by the sequencing machines and
-undergoes a processing procedure that consists on a quality control, the
-mapping and variant calling steps that result in a file containing the
-set of variants in the sample. From this point, the prioritization
-component or the diagnostic component can be launched.Â
-
-
-
-*Figure 1.** *OMICS MASTER solution overview. Data is produced in the
-external labs and comes to IT4I (represented by the blue dashed line).
-The data pre-processor converts raw data into a list of variants and
-annotations for each sequenced patient. These lists files together with
-primary and secondary (alignment) data files are stored in IT4I sequence
-DB and uploaded to the discovery (candidate prioritization) or
-diagnostic component where they can be analyzed directly by the user
-that produced them, depending of the experimental design carried
-out*. style="text-align: left; ">Â
-
-Typical genomics pipelines are composed by several components that need
-to be launched manually. The advantage of OMICS MASTER pipeline is that
-all these components are invoked sequentially in an automated way.
-
-OMICS MASTER pipeline inputs a FASTQ file and outputs an enriched VCF
-file. This pipeline is able to queue all the jobs to PBS by only
-launching a process taking all the necessary input files and creates the
-intermediate and final folders
-
-Let’s see each of the OMICS MASTER solution components:
-
-Components
-----------
-
-### Processing
-
-This component is composed by a set of programs that carry out quality
-controls, alignment, realignment, variant calling and variant
-annotation. It turns raw data from the sequencing machine into files
-containing lists of variants (VCF) that once annotated, can be used by
-the following components (discovery and diagnosis).
-
-We distinguish three types of sequencing instruments: bench sequencers
-(MySeq, IonTorrent, and Roche Junior, although this last one is about
-being discontinued), which produce relatively Genomes in the clinic
-
-low throughput (tens of million reads), and high end sequencers, which
-produce high throughput (hundreds of million reads) among which we have
-Illumina HiSeq 2000 (and new models) and SOLiD. All of them but SOLiD
-produce data in sequence format. SOLiD produces data in a special format
-called colour space that require of specific software for the mapping
-process. Once the mapping has been done, the rest of the pipeline is
-identical. Anyway, SOLiD is a technology which is also about being
-discontinued by the manufacturer so, this type of data will be scarce in
-the future.
-
-#### Quality control, preprocessing and statistics for FASTQ
-
- FastQC& FastQC.
-
-These steps are carried out over the original FASTQ file with optimized
-scripts and includes the following steps: sequence cleansing, estimation
-of base quality scores, elimination of duplicates and statistics.
-
-Input: FASTQ file.
-
-Output: FASTQ file plus an HTML file containing statistics on the
-data.Â
-
-FASTQ formatÂ
-It represents the nucleotide sequence and its corresponding
-quality scores.
-
-
-*Figure 2.**FASTQ file.**
-
-#### Mapping
-
-Component:** Hpg-aligner.**
-
-Sequence reads are mapped over the human reference genome. SOLiD reads
-are not covered by this solution; they should be mapped with specific
-software (among the few available options, SHRiMP seems to be the best
-one). For the rest of NGS machine outputs we use HPG Aligner.
-HPG-Aligner is an innovative solution, based on a combination of mapping
-with BWT and local alignment with Smith-Waterman (SW), that drastically
-increases mapping accuracy (97% versus 62-70% by current mappers, in the
-most common scenarios). This proposal provides a simple and fast
-solution that maps almost all the reads, even those containing a high
-number of mismatches or indels.
-
-Input: FASTQ file.
-
-Output:** Aligned file in BAM format.***
-
-Sequence Alignment/Map (SAM)**
-
-It is a human readable tab-delimited format in which each read and
-its alignment is represented on a single line. The format can represent
-unmapped reads, reads that are mapped to unique locations, and reads
-that are mapped to multiple locations.
-
-The SAM format (1)^> consists of one header
-section and one alignment section. The lines in the header section start
-with character â€@’, and lines in the alignment section do not. All lines
-are TAB delimited.
-
-In SAM, each alignment line has 11 mandatory fields and a variable
-number of optional fields. The mandatory fields are briefly described in
-Table 1. They must be present but their value can be a
-â€*’> or a zero (depending on the field) if the
-corresponding information is unavailable. Â
-
-<col width="33%" />
-<col width="33%" />
-<col width="33%" />
- |<strong>No.</strong>\ |<p><strong>Name</strong>\ |<p><strong>Description</strong></p> |
- |1\ |<p>QNAME\ |<p>Query NAME of the read or the read pair</p> |
- |2\ |<p>FLAG\ |<p>Bitwise FLAG (pairing,strand,mate strand,etc.)</p> |
- |3\ |<p>RNAMEÂ \ |<p>Reference sequence NAME</p> |
- |4\ |<p>POS \ |<p>1-Based  leftmost POSition of clipped alignment</p> |
- |5\ |<p>MAPQÂ \ |<p>MAPping Quality (Phred-scaled)</p> |
- |6\ |<p>CIGARÂ \ |<p>Extended CIGAR string (operations:MIDNSHP)</p> |
- |7\ |<p>MRNMÂ \ |<p>Mate REference NaMe ('=' if same RNAME)</p> |
- |8\ |<p>MPOSÂ \ |<p>1-Based leftmost Mate POSition</p> |
- |9\ |<p>ISIZEÂ \ |<p>Inferred Insert SIZEÂ </p> |
- |10\ |<p>SEQÂ \ |<p>Query SEQuence on the same strand as the reference</p> |
- |11\ |<p>QUALÂ \ |<p>Query QUALity (ASCII-33=Phred base quality)</p> |
-
-*Table 1.** *Mandatory fields in the SAM format.
-
-The standard CIGAR description of pairwise alignment defines three
-operations: â€M’ for match/mismatch, â€I’ for insertion compared with the
-reference and â€D’ for deletion. The extended CIGAR proposed in SAM added
-four more operations: â€N’ for skipped bases on the reference, â€S’ for
-soft clipping, â€H’ for hard clipping and â€P’ for padding. These support
-splicing, clipping, multi-part and padded alignments. Figure 3 shows
-examples of CIGAR strings for different types of alignments.Â
-
-
-*
-Figure 3.** *SAM format file. The â€@SQ’ line in the header section
-gives the order of reference sequences. Notably, r001 is the name of a
-read pair. According to FLAG 163 (=1+2+32+128), the read mapped to
-position 7 is the second read in the pair (128) and regarded as properly
-paired (1 + 2); its mate is mapped to 37 on the reverse strand (32).
-Read r002 has three soft-clipped (unaligned) bases. The coordinate shown
-in SAM is the position of the first aligned base. The CIGAR string for
-this alignment contains a P (padding) operation which correctly aligns
-the inserted sequences. Padding operations can be absent when an aligner
-does not support multiple sequence alignment. The last six bases of read
-r003 map to position 9, and the first five to position 29 on the reverse
-strand. The hard clipping operation H indicates that the clipped
-sequence is not present in the sequence field. The NM tag gives the
-number of mismatches. Read r004 is aligned across an intron, indicated
-by the N operation.**
-
-Binary Alignment/Map (BAM)**
-
-BAM is the binary representation of SAM and keeps exactly the same
-information as SAM. BAM uses lossless compression to reduce the size of
-the data by about 75% and provides an indexing system that allows reads
-that overlap a region of the genome to be retrieved and rapidly
-traversed.Â
-
-#### Quality control, preprocessing and statistics for BAM
-
-Component:** Hpg-Fastq & FastQC. Some features:
-
-- Quality control: % reads with N errors, % reads with multiple
- mappings, strand bias, paired-end insert, ...
-- Filtering: by number of errors, number of hits, …
- - Comparator: stats, intersection, ...
-
-Input:** BAM** file.**
-
-Output:** BAM file plus an HTML file containing statistics.**
-
-#### Variant Calling
-
-Component:** GATK.**
-
-Identification of single nucleotide variants and indels on the
-alignments is performed using the Genome Analysis Toolkit (GATK). GATK
-(2)^ is a software package developed at the Broad Institute to analyze
-high-throughput sequencing data. The toolkit offers a wide variety of
-tools, with a primary focus on variant discovery and genotyping as well
-as strong emphasis on data quality assurance.
-
-Input:** BAM**
-
-Output:** VCF**
-
-**Variant Call Format (VCF)**
-
-VCF (3)^> is a standardized format for storing the
-most prevalent types of sequence variation, including SNPs, indels and
-larger structural variants, together with rich annotations. The format
-was developed with the primary intention to represent human genetic
-variation, but its use is not restricted >to diploid genomes
-and can be used in different contexts as well. Its flexibility and user
-extensibility allows representation of a wide variety of genomic
-variation with respect to a single reference sequence.
-
-A VCF file consists of a header section and a data section. The
-header contains an arbitrary number of metainformation lines, each
-starting with characters â€##’, and a TAB delimited field definition
-line, starting with a single â€#’ character. The meta-information header
-lines provide a standardized description of tags and annotations used in
-the data section. The use of meta-information allows the information
-stored within a VCF file to be tailored to the dataset in question. It
-can be also used to provide information about the means of file
-creation, date of creation, version of the reference sequence, software
-used and any other information relevant to the history of the file. The
-field definition line names eight mandatory columns, corresponding to
-data columns representing the chromosome (CHROM), a 1-based position of
-the start of the variant (POS), unique identifiers of the variant (ID),
-the reference allele (REF), a comma separated list of alternate
-non-reference alleles (ALT), a phred-scaled quality score (QUAL), site
-filtering information (FILTER) and a semicolon separated list of
-additional, user extensible annotation (INFO). In addition, if samples
-are present in the file, the mandatory header columns are followed by a
-FORMAT column and an arbitrary number of sample IDs that define the
-samples included in the VCF file. The FORMAT column is used to defineÂ
-the information contained within each subsequent genotype column, which
-consists of a colon separated list of fields. For example, the FORMAT
-field GT:GQ:DP in the fourth data entry of Figure 1a indicates that the
-subsequent entries contain information regarding the genotype, genotype
-quality and read depth for each sample. All data lines are TAB
-delimited and the number of fields in each data line must match the
-number of fields in the header line. It is strongly recommended that all
-annotation tags used are declared in the VCF header section.
-
-
-
-Figure 4.**> (a) Example of valid VCF. The header lines
-##fileformat and #CHROM are mandatory, the rest is optional but
-strongly recommended. Each line of the body describes variants present
-in the sampled population at one genomic position or region. All
-alternate alleles are listed in the ALT column and referenced from the
-genotype fields as 1-based indexes to this list; the reference haplotype
-is designated as 0. For multiploid data, the separator indicates whether
-the data are phased (|) or unphased (/). Thus, the two alleles C and G
-at the positions 2 and 5 in this figure occur on the same chromosome in
-SAMPLE1. The first data line shows an example of a deletion (present in
-SAMPLE1) and a replacement of two bases by another base (SAMPLE2); the
-second line shows a SNP and an insertion; the third a SNP; the fourth a
-large structural variant described by the annotation in the INFO column,
-the coordinate is that of the base before the variant. (b–f ) Alignments
-and VCF representations of different sequence variants: SNP, insertion,
-deletion, replacement, and a large deletion. The REF columns shows the
-reference bases replaced by the haplotype in the ALT column. The
-coordinate refers to the first reference base. (g) Users are advised to
-use simplest representation possible and lowest coordinate in cases
-where the position is ambiguous.
-
-###Annotating
-
-Component:** HPG-Variant
-
-The functional consequences of every variant found are then annotated
-using the HPG-Variant software, which extracts from CellBase**,** the
-Knowledge database, all the information relevant on the predicted
-pathologic effect of the variants.
-
-VARIANT (VARIant Analysis Tool) (4)^ reports information on the
-variants found that include consequence type and annotations taken from
-different databases and repositories (SNPs and variants from dbSNP and
-1000 genomes, and disease-related variants from the Genome-Wide
-Association Study (GWAS) catalog, Online Mendelian Inheritance in Man
-(OMIM), Catalog of Somatic Mutations in Cancer (COSMIC) mutations, etc.
-VARIANT also produces a rich variety of annotations that include
-information on the regulatory (transcription factor or miRNAbinding
-sites, etc.) or structural roles, or on the selective pressures on the
-sites affected by the variation. This information allows extending the
-conventional reports beyond the coding regions and expands the knowledge
-on the contribution of non-coding or synonymous variants to the
-phenotype studied.
-
-Input:** VCF**
-
-Output:** The output of this step is the Variant Calling Format (VCF)
-file, which contains changes with respect to the reference genome with
-the corresponding QC and functional annotations.**
-
-#### CellBase
-
-CellBase(5)^Â is a relational database integrates biological information
-from different sources and includes:
-
-**Core features:**
-
-We took genome sequences, genes, transcripts, exons, cytobands or cross
-references (xrefs) identifiers (IDs)Â >from Ensembl
-(6)^>. Protein information including sequences, xrefs or
-protein features (natural variants, mutagenesis sites,
-post-translational modifications, etc.) were imported from UniProt
-(7)^>.
-
-**Regulatory:**
-
-CellBase imports miRNA from miRBase (8)^; curated and non-curated miRNA
-targets from miRecords (9)^, >miRTarBase ^(10)^>,
-TargetScan(11)^> and microRNA.org ^(12)^> and
-CpG islands and conserved regions from the UCSC database
-(13)^>.>
-
-**Functional annotation**
-
-OBO Foundry (14)^ develops many biomedical ontologies that are
-implemented in OBO format. We designed a SQL schema to store these OBO
-ontologies and >30 ontologies were imported. OBO ontology term
-annotations were taken from Ensembl (6)^. InterPro ^(15)^ annotations
-were also imported.
-
-**Variation**
-
-CellBase includes SNPs from dbSNP (16)^; SNP population frequencies
-from HapMap (17)^, 1000 genomes project ^(18)^ and Ensembl ^(6)^;
-phenotypically annotated SNPs were imported from NHRI GWAS Catalog
-(19)^,^Â ^>HGMD ^(20)^>, Open Access GWAS Database
-(21)^>, UniProt ^(7)^> and OMIM
-(22)^>; mutations from COSMICÂ ^(23)^> and
-structural variations from Ensembl
-(6)^>.>
-
-**Systems biology**
-
-We also import systems biology information like interactome information
-from IntAct (24)^. Reactome ^(25)^> stores pathway and interaction
-information in BioPAX (26)^> format. BioPAX data exchange
-format >enables the integration of diverse pathway
-resources. We successfully solved the problem of storing data released
-in BioPAX format into a SQL relational schema, which allowed us
-importing Reactome in CellBase.
-
-### [Diagnostic component (TEAM)](diagnostic-component-team.html)
-
-### [Priorization component (BiERApp)](priorization-component-bierapp.html)
-
-Usage
------
-
-First of all, we should load ngsPipeline
-module:
-
- $ module load ngsPipeline
-
-This command will load python/2.7.5
-module and all the required modules (
-hpg-aligner,
-gatk, etc)
-
- If we launch ngsPipeline with â€-h’, we will get the usage
-help:Â
-
- $ ngsPipeline -h
- Usage: ngsPipeline.py [-h] -i INPUT -o OUTPUT -p PED --project PROJECT --queue
- Â Â Â Â Â Â Â Â Â Â Â QUEUE [--stages-path STAGES_PATH] [--email EMAIL]
- [--prefix PREFIX] [-s START] [-e END] --log
-
- Python pipeline
-
- optional arguments:
-  -h, --help       show this help message and exit
- Â -i INPUT, --input INPUT
- Â -o OUTPUT, --output OUTPUT
- Â Â Â Â Â Â Â Â Â Â Â Â Output Data directory
- Â -p PED, --ped PED Â Â Ped file with all individuals
- Â --project PROJECT Â Â Project Id
- Â --queue QUEUE Â Â Â Â Queue Id
- Â --stages-path STAGES_PATH
- Â Â Â Â Â Â Â Â Â Â Â Â Custom Stages path
- Â --email EMAIL Â Â Â Â Email
- Â --prefix PREFIX Â Â Â Prefix name for Queue Jobs name
- Â -s START, --start START
- Â Â Â Â Â Â Â Â Â Â Â Â Initial stage
- Â -e END, --end END Â Â Final stage
-  --log         Log to file
-
-Â
-
-Let us see a brief description of the arguments:
-
-Â Â Â Â Â *-h --help*. Show the help.
-
-Â Â Â Â Â *-i, --input.* The input data directory. This directory must to
-have a special structure. We have to create one folder per sample (with
-the same name). These folders will host the fastq files. These fastq
-files must have the following pattern “sampleName” + “_” + “1 or 2” +
-“.fq”. 1 for the first pair (in paired-end sequences), and 2 for the
-second one.
-
-Â Â Â Â Â *-o , --output.* The output folder. This folder will contain all
-the intermediate and final folders. When the pipeline will be executed
-completely, we could remove the intermediate folders and keep only the
-final one (with the VCF file containing all the variants)
-
-Â Â Â Â Â *-p , --ped*. The ped file with the pedigree. This file contains
-all the sample names. These names must coincide with the names of the
-input folders. If our input folder contains more samples than the .ped
-file, the pipeline will use only the samples from the .ped file.
-
-Â Â Â Â Â *--email.* Email for PBS notifications.
-
-Â Â Â Â Â *--prefix.* Prefix for PBS Job names.
-
-Â Â Â Â *-s, --start & -e, --end.* Â Initial and final stage. If we want to
-launch the pipeline in a specific stage we must use -s. If we want to
-end the pipeline in a specific stage we must use -e.
-
-Â Â Â Â Â *--log*. Using log argument NGSpipeline will prompt all the logs
-to this file.
-
-Â Â Â Â *--project*>. Project ID of your supercomputer
-allocation.Â
-
-Â Â Â Â *--queue*.
-[Queue](../../resource-allocation-and-job-execution/introduction.html)
-to run the jobs in.
-
-Â >Input, output and ped arguments are mandatory. If the output
-folder does not exist, the pipeline will create it.
-
-Examples
----------------------
-
-This is an example usage of NGSpipeline:
-
-We have a folder with the following structure in >
-/apps/bio/omics/1.0/sample_data/Â >:
-
- /apps/bio/omics/1.0/sample_data
- └── data
- ├── file.ped
- ├── sample1
- │  ├── sample1_1.fq
- │  └── sample1_2.fq
- └── sample2
- ├── sample2_1.fq
- └── sample2_2.fq
-
-The ped file ( file.ped) contains the
-following info:>Â
-
- #family_ID sample_ID parental_ID maternal_ID sex phenotype
- FAM sample_A 0 0 1 1
- FAM sample_B 0 0 2 2
-
-Now, lets load the NGSPipeline module and copy the sample data to a
-[scratch directory](../../storage.html) :
-
- $ module load ngsPipeline
- $ mkdir -p /scratch/$USER/omics/results
- $ cp -r /apps/bio/omics/1.0/sample_data /scratch/$USER/omics/
-
-Now, we can launch the pipeline (replace OPEN-0-0 with your Project ID)
-:
-
- $ ngsPipeline -i /scratch/$USER/omics/sample_data/data -o /scratch/$USER/omics/results -p /scratch/$USER/omics/sample_data/data/file.ped --project OPEN-0-0 --queue qprod
-
-This command submits the processing [jobs to the
-queue](../../resource-allocation-and-job-execution/job-submission-and-execution.html).Â
-
-If we want to re-launch the pipeline from stage 4 until stage 20 we
-should use the next command:
-
- $ ngsPipeline -i /scratch/$USER/omics/sample_data/data -o /scratch/$USER/omics/results -p /scratch/$USER/omics/sample_data/data/file.ped -s 4 -e 20 --project OPEN-0-0 --queue qprod
-
-Details on the pipeline
-------------------------------------
-
-The pipeline calls the following tools:
-
-- >[fastqc](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/),
- a>Â quality control tool for high throughput
- sequence data.
-- >[gatk](https://www.broadinstitute.org/gatk/), >The
- Genome Analysis Toolkit or GATK is a software package developed at
- the Broad Institute to analyze high-throughput sequencing data. The
- toolkit offers a wide variety of tools, with a primary focus on
- variant discovery and genotyping as well as strong emphasis on data
- quality assurance. Its robust architecture, powerful processing
- engine and high-performance computing features make it capable of
- taking on projects of any size.
-- >[hpg-aligner](http://wiki.opencb.org/projects/hpg/doku.php?id=aligner:downloads), >HPG
- Aligner has been designed to align short and long reads with high
- sensitivity, therefore any number of mismatches or indels
- are allowed. HPG Aligner implements and combines two well known
- algorithms:Â *Burrows-Wheeler Transform*>Â (BWT) to
- speed-up mapping high-quality reads,
- and *Smith-Waterman*> (SW) to increase sensitivity when
- reads cannot be mapped using BWT.
-- >[hpg-fastq](http://docs.bioinfo.cipf.es/projects/fastqhpc/wiki), > a
- quality control tool for high throughput
- sequence data.
-- >[hpg-variant](http://wiki.opencb.org/projects/hpg/doku.php?id=variant:downloads), >The
- HPG Variant suite is an ambitious project aimed to provide a
- complete suite of tools to work with genomic variation data, from
- VCF tools to variant profiling or genomic statistics. It is being
- implemented using High Performance Computing technologies to provide
- the best performance possible.
-- >[picard](http://picard.sourceforge.net/), >Picard
- comprises Java-based command-line utilities that manipulate SAM
- files, and a Java API (HTSJDK) for creating new programs that read
- and write SAM files. Both SAM text format and SAM binary (BAM)
- format are supported.
-- >[samtools](http://samtools.sourceforge.net/samtools-c.shtml), >SAM
- Tools provide various utilities for manipulating alignments in the
- SAM format, including sorting, merging, indexing and generating
- alignments in a
- per-position format.
-- >>[snpEff](http://snpeff.sourceforge.net/), <span>Genetic
- variant annotation and effect
- prediction toolbox.
-
-This listing show which tools are used in each step of the pipeline :
-
-- >stage-00: fastqc
-- >stage-01: hpg_fastq
-- >stage-02: fastqc
-- >stage-03: hpg_aligner and samtools
-- >stage-04: samtools
-- >stage-05: samtools
-- >stage-06: fastqc
-- >stage-07: picard
-- >stage-08: fastqc
-- >stage-09: picard
-- >stage-10: gatk
-- >stage-11: gatk
-- >stage-12: gatk
-- >stage-13: gatk
-- >stage-14: gatk
-- >stage-15: gatk
-- >stage-16: samtools
-- >stage-17: samtools
-- >stage-18: fastqc
-- >stage-19: gatk
-- >stage-20: gatk
-- >stage-21: gatk
-- >stage-22: gatk
-- >stage-23: gatk
-- >stage-24: hpg-variant
-- >stage-25: hpg-variant
-- >stage-26: snpEff
-- >stage-27: snpEff
-- >stage-28: hpg-variant
-
-Interpretation
----------------------------
-
-The output folder contains all the subfolders with the intermediate
-data. This folder contains the final VCF with all the variants. This
-file can be uploaded into
-[TEAM](diagnostic-component-team.html) by using the VCF
-file button. It is important to note here that the entire management of
-the VCF file is local: no patient’s sequence data is sent over the
-Internet thus avoiding any problem of data privacy or confidentiality.
-
-
-
-*Figure 7**. *TEAM upload panel.* *Once the file has been uploaded, a
-panel must be chosen from the Panel *** list. Then, pressing the Run
-button the diagnostic process starts.*
-
-Once the file has been uploaded, a panel must be chosen from the Panel
-list. Then, pressing the Run button the diagnostic process starts. TEAM
-searches first for known diagnostic mutation(s) taken from four
-databases: HGMD-public (20)^,
-[HUMSAVAR](http://www.uniprot.org/docs/humsavar),
-ClinVar (29)^ and COSMIC ^(23)^.
-
-
-
-*Figure 7.** *The panel manager. The elements used to define a panel
-are (**A**) disease terms, (**B**) diagnostic mutations and (**C**)
-genes. Arrows represent actions that can be taken in the panel manager.
-Panels can be defined by using the known mutations and genes of a
-particular disease. This can be done by dragging them to the **Primary
-Diagnostic** box (action **D**). This action, in addition to defining
-the diseases in the **Primary Diagnostic** box, automatically adds the
-corresponding genes to the **Genes** box. The panels can be customized
-by adding new genes (action **F**) or removing undesired genes (action
-G**). New disease mutations can be added independently or associated
-to an already existing disease term (action **E**). Disease terms can be
-removed by simply dragging them back (action **H**).*
-
-For variant discovering/filtering we should upload the VCF file into
-BierApp by using the following form:
-
-**
-
-**Figure 8.** *BierApp VCF upload panel. It is recommended to choose
-a name for the job as well as a description.**
-
-Each prioritization (â€job’) has three associated screens that facilitate
-the filtering steps. The first one, the â€Summary’ tab, displays a
-statistic of the data set analyzed, containing the samples analyzed, the
-number and types of variants found and its distribution according to
-consequence types. The second screen, in the â€Variants and effect’ tab,
-is the actual filtering tool, and the third one, the â€Genome view’ tab,
-offers a representation of the selected variants within the genomic
-context provided by an embedded version of >the Genome Maps Tool
-(30)^>.
-
-
-
-**Figure 9.*** *This picture shows all the information associated to
-the variants. If a variant has an associated phenotype we could see it
-in the last column. In this case, the variant 7:132481242 C>T is
-associated to the phenotype: large intestine tumor.**
-
-*
-*
-
-References
------------------------
-
-1. Heng Li, Bob Handsaker, Alec Wysoker, Tim
- Fennell, Jue Ruan, Nils Homer, Gabor Marth5, Goncalo Abecasis6,
- Richard Durbin and 1000 Genome Project Data Processing Subgroup: The
- Sequence Alignment/Map format and SAMtools. Bioinformatics 2009,
- 25: 2078-2079.
-2. >McKenna A, Hanna M, Banks E, Sivachenko
- A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S,
- Daly M, DePristo MA: The Genome Analysis Toolkit: a MapReduce
- framework for analyzing next-generation DNA sequencing data.
- *Genome Res* >2010, 20:1297-1303.
-3. Petr Danecek, Adam Auton, Goncalo Abecasis,
- Cornelis A. Albers, Eric Banks, Mark A. DePristo, Robert E.
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- McVean, Richard Durbin, and 1000 Genomes Project Analysis Group. The
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- 2012, 40:W54-58.
-5. Bleda M, Tarraga J, de Maria A, Salavert F,
- Garcia-Alonso L, Celma M, Martin A, Dopazo J, Medina I: CellBase, a
- comprehensive collection of RESTful web services for retrieving
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- Fairley,S., Fitzgerald,S. et al. (2012) Ensembl 2012. Nucleic Acids
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-8. Kozomara,A. and Griffiths-Jones,S. (2011)
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- 40, D918–D923.
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- Bug,W., Ceusters,W., Goldberg,L.J., Eilbeck,K.,
- Ireland,A., Mungall,C.J. et al. (2007) The OBO Foundry: coordinated
- evolution of ontologies to support biomedical data integration. Nat.
- Biotechnol., 25, 1251–1255.
-15. Hunter,S., Jones,P., Mitchell,A.,
- Apweiler,R., Attwood,T.K.,Bateman,A., Bernard,T., Binns,D.,
- Bork,P., Burge,S. et al. (2012) InterPro in 2011: new developments
- in the family and domain prediction database. Nucleic Acids Res.,
- 40, D306–D312.
-16. Sherry,S.T., Ward,M.H., Kholodov,M.,
- Baker,J., Phan,L., Smigielski,E.M. and Sirotkin,K. (2001) dbSNP: the
- NCBI database of genetic variation. Nucleic Acids Res.,
- 29, 308–311.
-17. Altshuler,D.M., Gibbs,R.A., Peltonen,L.,
- Dermitzakis,E., Schaffner,S.F., Yu,F., Bonnen,P.E., de Bakker,P.I.,
- Deloukas,P., Gabriel,S.B. et al. (2010) Integrating common and rare
- genetic variation in diverse human populations. Nature,
- 467, 52–58.
-18. 1000 Genomes Project Consortium. (2010) A map
- of human genome variation from population-scale sequencing. Nature,
- 467, 1061–1073.
-19. Hindorff,L.A., Sethupathy,P., Junkins,H.A.,
- Ramos,E.M., Mehta,J.P., Collins,F.S. and Manolio,T.A. (2009)
- Potential etiologic and functional implications of genome-wide
- association loci for human diseases and traits. Proc. Natl Acad.
- Sci. USA, 106, 9362–9367.
-20. Stenson,P.D., Ball,E.V., Mort,M.,
- Phillips,A.D., Shiel,J.A., Thomas,N.S., Abeysinghe,S., Krawczak,M.
- and Cooper,D.N. (2003) Human gene mutation database (HGMD):
- 2003 update. Hum. Mutat., 21, 577–581.
-21. Johnson,A.D. and O’Donnell,C.J. (2009) An
- open access database of genome-wide association results. BMC Med.
- Genet, 10, 6.
-22. McKusick,V. (1998) A Catalog of Human Genes
- and Genetic Disorders, 12th edn. John Hopkins University
- Press,Baltimore, MD.
-23. Forbes,S.A., Bindal,N., Bamford,S., Cole,C.,
- Kok,C.Y., Beare,D., Jia,M., Shepherd,R., Leung,K., Menzies,A. et al.
- (2011) COSMIC: mining complete cancer genomes in the catalogue of
- somatic mutations in cancer. Nucleic Acids Res.,
- 39, D945–D950.
-24. Kerrien,S., Aranda,B., Breuza,L., Bridge,A.,
- Broackes-Carter,F., Chen,C., Duesbury,M., Dumousseau,M.,
- Feuermann,M., Hinz,U. et al. (2012) The Intact molecular interaction
- database in 2012. Nucleic Acids Res., 40, D841–D846.
-25. Croft,D., O’Kelly,G., Wu,G., Haw,R.,
- Gillespie,M., Matthews,L., Caudy,M., Garapati,P.,
- Gopinath,G., Jassal,B. et al. (2011) Reactome: a database of
- reactions, pathways and biological processes. Nucleic Acids Res.,
- 39, D691–D697.
-26. Demir,E., Cary,M.P., Paley,S., Fukuda,K.,
- Lemer,C., Vastrik,I.,Wu,G., D’Eustachio,P., Schaefer,C., Luciano,J.
- et al. (2010) The BioPAX community standard for pathway
- data sharing. Nature Biotechnol., 28, 935–942.
-27. Alemán Z, GarcĂa-GarcĂa F, Medina I, Dopazo J
- (2014): A web tool for the design and management of panels of genes
- for targeted enrichment and massive sequencing for
- clinical applications. Nucleic Acids Res 42: W83-7.
-28. [Alemán
- A](http://www.ncbi.nlm.nih.gov/pubmed?term=Alem%C3%A1n%20A%5BAuthor%5D&cauthor=true&cauthor_uid=24803668)>, [Garcia-Garcia
- F](http://www.ncbi.nlm.nih.gov/pubmed?term=Garcia-Garcia%20F%5BAuthor%5D&cauthor=true&cauthor_uid=24803668)>, [Salavert
- F](http://www.ncbi.nlm.nih.gov/pubmed?term=Salavert%20F%5BAuthor%5D&cauthor=true&cauthor_uid=24803668)>, [Medina
- I](http://www.ncbi.nlm.nih.gov/pubmed?term=Medina%20I%5BAuthor%5D&cauthor=true&cauthor_uid=24803668)>, [Dopazo
- J](http://www.ncbi.nlm.nih.gov/pubmed?term=Dopazo%20J%5BAuthor%5D&cauthor=true&cauthor_uid=24803668)> (2014).
- A web-based interactive framework to assist in the prioritization of
- disease candidate genes in whole-exome sequencing studies.
- [Nucleic
- Acids Res.](http://www.ncbi.nlm.nih.gov/pubmed/?term=BiERapp "Nucleic acids research.")>42 :W88-93.
-29. Landrum,M.J., Lee,J.M., Riley,G.R., Jang,W.,
- Rubinstein,W.S., Church,D.M. and Maglott,D.R. (2014) ClinVar: public
- archive of relationships among sequence variation and
- human phenotype. Nucleic Acids Res., 42, D980–D985.
-30. Medina I, Salavert F, Sanchez R, de Maria A,
- Alonso R, Escobar P, Bleda M, Dopazo J: Genome Maps, a new
- generation genome browser. Nucleic Acids Res 2013, 41:W41-46.
-
-Â
-
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-Priorization component (BiERApp)
-================================
-
-### Access
-
-BiERApp is available at the following address
-:Â <http://omics.it4i.cz/bierapp/>
-
-The address is accessible only
-via [VPN. ](../../accessing-the-cluster/vpn-access.html)
-
-###BiERApp
-
-###This tool is aimed to discover new disease genes or variants by studying affected families or cases and controls. It carries out a filtering process to sequentially remove:Â (i) variants which are not no compatible with the disease because are not expected to have impact on the protein function; (ii) variants that exist at frequencies incompatible with the disease; (iii) variants that do not segregate with the disease. The result is a reduced set of disease gene candidates that should be further validated experimentally.
-
-BiERapp >(28) efficiently helps in the identification of
-causative variants in family and sporadic genetic diseases. The program
-reads lists of predicted variants (nucleotide substitutions and indels)
-in affected individuals or tumor samples and controls. In family
-studies, different modes of inheritance can easily be defined to filter
-out variants that do not segregate with the disease along the family.
-Moreover, BiERapp integrates additional information such as allelic
-frequencies in the general population and the most popular damaging
-scores to further narrow down the number of putative variants in
-successive filtering steps. BiERapp provides an interactive and
-user-friendly interface that implements the filtering strategy used in
-the context of a large-scale genomic project carried out by the Spanish
-Network for Research, in Rare Diseases (CIBERER) and the Medical Genome
-Project. in which more than 800 exomes have been analyzed.
-
-
-
-*Figure 6**. *Web interface to the prioritization tool.* *This
-figure*Â *shows the interface of the web tool for candidate gene
-prioritization with the filters available. The tool includes a genomic
-viewer (Genome Maps >30) that enables the representation of
-the variants in the corresponding genomic coordinates.*
-
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--- a/converted/docs.it4i.cz/anselm-cluster-documentation/software/openfoam.md
+++ /dev/null
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-OpenFOAM
-========
-
-A free, open source CFD software package
-
-
-
-Introduction**
-----------------
-
-OpenFOAM is a free, open source CFD software package developed
-by [**OpenCFD Ltd**](http://www.openfoam.com/about) at [**ESI
-Group**](http://www.esi-group.com/) and distributed by the [**OpenFOAM
-Foundation **](http://www.openfoam.org/). It has a large user base
-across most areas of engineering and science, from both commercial and
-academic organisations.Â
-
-Homepage: <http://www.openfoam.com/>
-
-###Installed version**
-
-Currently, several version compiled by GCC/ICC compilers in
-single/double precision with several version of openmpi are available on
-Anselm.
-
-For example syntax of available OpenFOAM module is:
-
-< openfoam/2.2.1-icc-openmpi1.6.5-DP >
-
-this means openfoam version >2.2.1 compiled by
-ICC compiler with >openmpi1.6.5 in> double
-precision.
-
-Naming convection of the installed versions is following:
-
-Â Â
-openfoam/<>VERSION>>-<>COMPILER<span>>-<</span><span>openmpiVERSION</span><span>>-<</span><span>PRECISION</span><span>></span>
-
-- ><>VERSION>> - version of
- openfoam
-- ><>COMPILER> - version of used
- compiler
-- ><>openmpiVERSION> - version of used
- openmpi/impi
-- ><>PRECISION> - DP/>SP –
- double/single precision
-
-###Available OpenFOAM modules**
-
-To check available modules use
-
- $ module avail
-
-In /opt/modules/modulefiles/engineering you can see installed
-engineering softwares:
-
- ------------------------------------ /opt/modules/modulefiles/engineering -------------------------------------------------------------
- ansys/14.5.x              matlab/R2013a-COM                               openfoam/2.2.1-icc-impi4.1.1.036-DP
- comsol/43b-COMÂ Â Â Â Â Â Â Â Â Â Â Â matlab/R2013a-EDUÂ Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â openfoam/2.2.1-icc-openmpi1.6.5-DP
- comsol/43b-EDUÂ Â Â Â Â Â Â Â Â Â Â Â openfoam/2.2.1-gcc481-openmpi1.6.5-DPÂ Â Â Â Â Â Â Â Â Â Â paraview/4.0.1-gcc481-bullxmpi1.2.4.1-osmesa10.0
- lsdyna/7.x.x              openfoam/2.2.1-gcc481-openmpi1.6.5-SP
-
-For information how to use modules please [look
-here](../environment-and-modules.html "Environment and Modules ").
-
-Getting Started**
--------------------
-
-To create OpenFOAM environment on ANSELM give the commands:
-
- $ module load openfoam/2.2.1-icc-openmpi1.6.5-DP
-
- $ source $FOAM_BASHRC
-
-Pleas load correct module with your requirements “compiler - GCC/ICC,
-precision - DP/SP”.
-
-Create a project directory within the $HOME/OpenFOAM directory
-named ><USER>-<OFversion> and create a directory
-named run within it, e.g. by typing:
-
- $ mkdir -p $FOAM_RUN
-
-Project directory is now available by typing:
-
- $ cd /home/<USER>/OpenFOAM/<USER>-<OFversion>/run
-
-<OFversion> - for example <2.2.1>
-
-or
-
- $ cd $FOAM_RUN
-
-Copy the tutorial examples directory in the OpenFOAM distribution to
-the run directory:
-
- $ cp -r $FOAM_TUTORIALS $FOAM_RUN
-
-Now you can run the first case for example incompressible laminar flow
-in a cavity.
-
-Running Serial Applications**
--------------------------------
-
-Create a Bash script >test.shÂ
-
-
- #!/bin/bash
- module load openfoam/2.2.1-icc-openmpi1.6.5-DP
- source $FOAM_BASHRC
-
- # source to run functions
- . $WM_PROJECT_DIR/bin/tools/RunFunctions
-
- cd $FOAM_RUN/tutorials/incompressible/icoFoam/cavity
-
- runApplication blockMesh
- runApplication icoFoam
-
-
-
-
-
-Job submission
-
-
- $ qsub -A OPEN-0-0 -q qprod -l select=1:ncpus=16,walltime=03:00:00 test.sh
-
-
-
- For information about job submission please [look
-here](../resource-allocation-and-job-execution/job-submission-and-execution.html "Job submission").
-
-Running applications in parallel**
--------------------------------------------------
-
-Run the second case for example external incompressible turbulent
-flow - case - motorBike.
-
-First we must run serial application bockMesh and decomposePar for
-preparation of parallel computation.
-
-Create a Bash scrip test.sh:
-
-
- #!/bin/bash
- module load openfoam/2.2.1-icc-openmpi1.6.5-DP
- source $FOAM_BASHRC
-
- # source to run functions
- . $WM_PROJECT_DIR/bin/tools/RunFunctions
-
- cd $FOAM_RUN/tutorials/incompressible/simpleFoam/motorBike
-
- runApplication blockMesh
- runApplication decomposePar
-
-
-
-Job submission
-
-
- $ qsub -A OPEN-0-0 -q qprod -l select=1:ncpus=16,walltime=03:00:00 test.sh
-
-
-
-This job create simple block mesh and domain decomposition.
-Check your decomposition, and submit parallel computation:
-
-Create a PBS script>
-testParallel.pbs:
-
-
- #!/bin/bash
- #PBS -N motorBike
- #PBS -l select=2:ncpus=16
- #PBS -l walltime=01:00:00
- #PBS -q qprod
- #PBS -A OPEN-0-0
-
- module load openfoam/2.2.1-icc-openmpi1.6.5-DP
- source $FOAM_BASHRC
-
- cd $FOAM_RUN/tutorials/incompressible/simpleFoam/motorBike
-
- nproc = 32
-
- mpirun -hostfile ${PBS_NODEFILE} -np $nproc snappyHexMesh -overwrite -parallel | tee snappyHexMesh.log
-
- mpirun -hostfile ${PBS_NODEFILE} -np $nproc potentialFoam -noFunctionObject-writep -parallel | tee potentialFoam.log
-
- mpirun -hostfile ${PBS_NODEFILE} -np $nproc simpleFoam -parallel | tee simpleFoam.log
-
-
-
-nproc – number of subdomains
-
-Job submission
-
-
- $ qsub testParallel.pbs
-
-
-
-Compile your own solver**
-----------------------------------------
-
-Initialize OpenFOAM environment before compiling your solver
-
-
- $ module load openfoam/2.2.1-icc-openmpi1.6.5-DP
- $ source $FOAM_BASHRC
- $ cd $FOAM_RUN/
-
-Create directory applications/solvers in user directory
-
-
- $ mkdir -p applications/solvers
- $ cd applications/solvers
-
-
-
-Copy icoFoam solver’s source files
-
-
- $ cp -r $FOAM_SOLVERS/incompressible/icoFoam/ My_icoFoam
- $ cd My_icoFoam
-
-Rename icoFoam.C to My_icoFOAM.C
-
-
- $ mv icoFoam.C My_icoFoam.C
-
-
-
-Edit >*files* file in *Make* directory:
-
-
- icoFoam.C
- EXE = $(FOAM_APPBIN)/icoFoam
-
-and change to:
-
- My_icoFoam.C
- EXE = $(FOAM_USER_APPBIN)/My_icoFoam
-
-In directory My_icoFoam give the compilation command:
-
-
- $ wmake
-
-------------------------------------------------------------------------
-
-Â
-
-Â Have a fun with OpenFOAM :)**
-
-Â id="__caret">
-
-Â
-
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-Operating System
-================
-
-The operating system, deployed on ANSELM
-
-
-
-The operating system on Anselm is Linux - bullx Linux Server release
-6.3.
-
-bullx Linux is based on Red Hat Enterprise Linux. bullx Linux is a Linux
-distribution provided by Bull and dedicated to HPC applications.
-
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-ParaView
-========
-
-An open-source, multi-platform data analysis and visualization
-application
-
-
-
-Introduction
-------------
-
-ParaView**Â is an open-source, multi-platform data analysis and
-visualization application. ParaView users can quickly build
-visualizations to analyze their data using qualitative and quantitative
-techniques. The data exploration can be done interactively in 3D or
-programmatically using ParaView's batch processing capabilities.
-
-ParaView was developed to analyze extremely large datasets using
-distributed memory computing resources. It can be run on supercomputers
-to analyze datasets of exascale size as well as on laptops for smaller
-data.
-
-Homepage :Â <http://www.paraview.org/>
-
-Installed version
------------------
-
-Currently, version 4.0.1 compiled with GCC 4.8.1 against Bull MPI
-library and OSMesa 10.0 is installed on Anselm.
-
-Usage
------
-
-On Anselm, ParaView is to be used in client-server mode. A parallel
-ParaView server is launched on compute nodes by the user, and client is
-launched on your desktop PC to control and view the visualization.
-Download ParaView client application for your OS here
-:Â <http://paraview.org/paraview/resources/software.php>. Important :
-your version must match the version number installed on Anselm** !
-(currently v4.0.1)
-
-### Launching server
-
-To launch the server, you must first allocate compute nodes, for example
-:>Â
-
- $ qsub -I -q qprod -A OPEN-0-0 -l select=2
-
-to launch an interactive session on 2 nodes. Refer to [Resource
-Allocation and Job
-Execution](../resource-allocation-and-job-execution/introduction.html)
-for details.
-
-After the interactive session is opened, load the ParaView module :
-
- $ module add paraview
-
-Now launch the parallel server, with number of nodes times 16 processes
-:
-
- $ mpirun -np 32 pvserver --use-offscreen-rendering
- Waiting for client...
- Connection URL: cs://cn77:11111
- Accepting connection(s): cn77:11111
-
-Â Note the that the server is listening on compute node cn77 in this
-case, we shall use this information later.
-
-### Client connection
-
-Because a direct connection is not allowed to compute nodes on Anselm,
-you must establish a SSH tunnel to connect to the server. Choose a port
-number on your PC to be forwarded to ParaView server, for example 12345.
-If your PC is running Linux, use this command to estabilish a SSH tunnel
-:
-
- ssh -TN -L 12345:cn77:11111 username@anselm.it4i.cz
-
-replace username with your login and cn77
-with the name of compute node your ParaView server is running on (see
-previous step). If you use PuTTY on Windows, load Anselm connection
-configuration, t>hen go to Connection->
-SSH>->Tunnels to set up the
-port forwarding. Click Remote radio button. Insert 12345 to Source port
-textbox. Insert cn77:11111. Click Add button, then Open. [Read
-more about port
-forwarding.](https://docs.it4i.cz/anselm-cluster-documentation/software/resolveuid/11e53ad0d2fd4c5187537f4baeedff33)
-
-Now launch ParaView client installed on your desktop PC. Select
-File->Connect..., click Add Server. Fill in the following :
-
-Name : Anselm tunnel
-
-Server Type : Client/Server
-
-Host : localhost
-
-Port : 12345
-
-Click Configure, Save, the configuration is now saved for later use. Now
-click Connect to connect to the ParaView server. In your terminal where
-you have interactive session with ParaView server launched, you should
-see :
-
- Client connected.
-
-You can now use Parallel ParaView.
-
-### Close server
-
-Remember to close the interactive session after you finish working with
-ParaView server, as it will remain launched even after your client is
-disconnected and will continue to consume resources.
-
-GPU support
------------
-
-Currently, GPU acceleration is not supported in the server and ParaView
-will not take advantage of accelerated nodes on Anselm. Support for GPU
-acceleration might be added in the future.
-
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-CESNET Data Storage
-===================
-
-
-
-Introduction
-------------
-
-Do not use shared filesystems at IT4Innovations as a backup for large
-amount of data or long-term archiving purposes.
-
-The IT4Innovations does not provide storage capacity for data archiving.
-Academic staff and students of research institutions in the Czech
-Republic can use [CESNET Storage
-service](https://du.cesnet.cz/).
-
-The CESNET Storage service can be used for research purposes, mainly by
-academic staff and students of research institutions in the Czech
-Republic.
-
-User of data storage CESNET (DU) association can become organizations or
-an individual person who is either in the current employment
-relationship (employees) or the current study relationship (students) to
-a legal entity (organization) that meets the “Principles for access to
-CESNET Large infrastructure (Access Policy)”.
-
-User may only use data storage CESNET for data transfer and storage
-which are associated with activities in science, research, development,
-the spread of education, culture and prosperity. In detail see
-“Acceptable Use Policy CESNET Large Infrastructure (Acceptable Use
-Policy, AUP)”.
-
-The service is documented at
-<https://du.cesnet.cz/wiki/doku.php/en/start>. For special requirements
-please contact directly CESNET Storage Department via e-mail
-[du-support(at)cesnet.cz](mailto:du-support@cesnet.cz).
-
-The procedure to obtain the CESNET access is quick and trouble-free.
-
-(source
-[https://du.cesnet.cz/](https://du.cesnet.cz/wiki/doku.php/en/start "CESNET Data Storage"))
-
-CESNET storage access
----------------------
-
-### Understanding Cesnet storage
-
-It is very important to understand the Cesnet storage before uploading
-data. Please read
-<https://du.cesnet.cz/en/navody/home-migrace-plzen/start> first.
-
-Once registered for CESNET Storage, you may [access the
-storage](https://du.cesnet.cz/en/navody/faq/start) in
-number of ways. We recommend the SSHFS and RSYNC methods.
-
-### SSHFS Access
-
-SSHFS: The storage will be mounted like a local hard drive
-
-The SSHFSÂ provides a very convenient way to access the CESNET Storage.
-The storage will be mounted onto a local directory, exposing the vast
-CESNET Storage as if it was a local removable harddrive. Files can be
-than copied in and out in a usual fashion.
-
-First, create the mountpoint
-
- $ mkdir cesnet
-
-Mount the storage. Note that you can choose among the ssh.du1.cesnet.cz
-(Plzen), ssh.du2.cesnet.cz (Jihlava), ssh.du3.cesnet.cz (Brno)
-Mount tier1_home **(only 5120M !)**:
-
- $ sshfs username@ssh.du1.cesnet.cz:. cesnet/
-
-For easy future access from Anselm, install your public key
-
- $ cp .ssh/id_rsa.pub cesnet/.ssh/authorized_keys
-
-Mount tier1_cache_tape for the Storage VO:
-
- $ sshfs username@ssh.du1.cesnet.cz:/cache_tape/VO_storage/home/username cesnet/
-
-View the archive, copy the files and directories in and out
-
- $ ls cesnet/
- $ cp -a mydir cesnet/.
- $ cp cesnet/myfile .
-
-Once done, please remember to unmount the storage
-
- $ fusermount -u cesnet
-
-### Rsync access
-
-Rsync provides delta transfer for best performance, can resume
-interrupted transfers
-
-Rsync is a fast and extraordinarily versatile file copying tool. It is
-famous for its delta-transfer algorithm, which reduces the amount of
-data sent over the network by sending only the differences between the
-source files and the existing files in the destination. Rsync is widely
-used for backups and mirroring and as an improved copy command for
-everyday use.
-
-Rsync finds files that need to be transferred using a "quick check"
-algorithm (by default) that looks for files that have changed in size or
-in last-modified time. Any changes in the other preserved attributes
-(as requested by options) are made on the destination file directly when
-the quick check indicates that the file's data does not need to be
-updated.
-
-More about Rsync at
-<https://du.cesnet.cz/en/navody/rsync/start#pro_bezne_uzivatele>
-
-Transfer large files to/from Cesnet storage, assuming membership in the
-Storage VO
-
- $ rsync --progress datafile username@ssh.du1.cesnet.cz:VO_storage-cache_tape/.
- $ rsync --progress username@ssh.du1.cesnet.cz:VO_storage-cache_tape/datafile .
-
-Transfer large directories to/from Cesnet storage, assuming membership
-in the Storage VO
-
- $ rsync --progress -av datafolder username@ssh.du1.cesnet.cz:VO_storage-cache_tape/.
- $ rsync --progress -av username@ssh.du1.cesnet.cz:VO_storage-cache_tape/datafolder .
-
-Transfer rates of about 28MB/s can be expected.
-
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-Storage
-=======
-
-
-
-There are two main shared file systems on Anselm cluster, the
-[HOME](../storage.html#home) and
-[SCRATCH](../storage.html#scratch). All login and compute
-nodes may access same data on shared filesystems. Compute nodes are also
-equipped with local (non-shared) scratch, ramdisk and tmp filesystems.
-
-Archiving
----------
-
-Please don't use shared filesystems as a backup for large amount of data
-or long-term archiving mean. The academic staff and students of research
-institutions in the Czech Republic can use [CESNET storage
-service](cesnet-data-storage.html), which is available
-via SSHFS.
-
-Shared Filesystems
-------------------
-
-Anselm computer provides two main shared filesystems, the [HOME
-filesystem](../storage.html#home) and the [SCRATCH
-filesystem](../storage.html#scratch). Both HOME and
-SCRATCH filesystems are realized as a parallel Lustre filesystem. Both
-shared file systems are accessible via the Infiniband network. Extended
-ACLs are provided on both Lustre filesystems for the purpose of sharing
-data with other users using fine-grained control.
-
-### Understanding the Lustre Filesystems
-
-(source <http://www.nas.nasa.gov>)
-
-A user file on the Lustre filesystem can be divided into multiple chunks
-(stripes) and stored across a subset of the object storage targets
-(OSTs) (disks). The stripes are distributed among the OSTs in a
-round-robin fashion to ensure load balancing.
-
-When a client (a compute
-node from your job) needs to create
-or access a file, the client queries the metadata server (
-MDS) and the metadata target (
-MDT) for the layout and location of the
-[file's
-stripes](http://www.nas.nasa.gov/hecc/support/kb/Lustre_Basics_224.html#striping).
-Once the file is opened and the client obtains the striping information,
-the MDS is no longer involved in the
-file I/O process. The client interacts directly with the object storage
-servers (OSSes) and OSTs to perform I/O operations such as locking, disk
-allocation, storage, and retrieval.
-
-If multiple clients try to read and write the same part of a file at the
-same time, the Lustre distributed lock manager enforces coherency so
-that all clients see consistent results.
-
-There is default stripe configuration for Anselm Lustre filesystems.
-However, users can set the following stripe parameters for their own
-directories or files to get optimum I/O performance:
-
-1. stripe_size: the size of the chunk in bytes; specify with k, m, or
- g to use units of KB, MB, or GB, respectively; the size must be an
- even multiple of 65,536 bytes; default is 1MB for all Anselm Lustre
- filesystems
-2. stripe_count the number of OSTs to stripe across; default is 1 for
- Anselm Lustre filesystems one can specify -1 to use all OSTs in
- the filesystem.
-3. stripe_offset The index of the
- OST where the first stripe is to be
- placed; default is -1 which results in random selection; using a
- non-default value is NOT recommended.
-
-Â
-
-Setting stripe size and stripe count correctly for your needs may
-significantly impact the I/O performance you experience.
-
-Use the lfs getstripe for getting the stripe parameters. Use the lfs
-setstripe command for setting the stripe parameters to get optimal I/O
-performance The correct stripe setting depends on your needs and file
-access patterns.Â
-
-`
-$ lfs getstripe dir|filename
-$ lfs setstripe -s stripe_size -c stripe_count -o stripe_offset dir|filename
-`
-
-Example:
-
-`
-$ lfs getstripe /scratch/username/
-/scratch/username/
-stripe_count: 1 stripe_size: 1048576 stripe_offset: -1
-
-$ lfs setstripe -c -1 /scratch/username/
-$ lfs getstripe /scratch/username/
-/scratch/username/
-stripe_count: 10 stripe_size: 1048576 stripe_offset: -1
-`
-
-In this example, we view current stripe setting of the
-/scratch/username/ directory. The stripe count is changed to all OSTs,
-and verified. All files written to this directory will be striped over
-10 OSTs
-
-Use lfs check OSTs to see the number and status of active OSTs for each
-filesystem on Anselm. Learn more by reading the man page
-
-`
-$ lfs check osts
-$ man lfs
-`
-
-### Hints on Lustre Stripping
-
-Increase the stripe_count for parallel I/O to the same file.
-
-When multiple processes are writing blocks of data to the same file in
-parallel, the I/O performance for large files will improve when the
-stripe_count is set to a larger value. The stripe count sets the number
-of OSTs the file will be written to. By default, the stripe count is set
-to 1. While this default setting provides for efficient access of
-metadata (for example to support the ls -l command), large files should
-use stripe counts of greater than 1. This will increase the aggregate
-I/O bandwidth by using multiple OSTs in parallel instead of just one. A
-rule of thumb is to use a stripe count approximately equal to the number
-of gigabytes in the file.
-
-Another good practice is to make the stripe count be an integral factor
-of the number of processes performing the write in parallel, so that you
-achieve load balance among the OSTs. For example, set the stripe count
-to 16 instead of 15 when you have 64 processes performing the writes.
-
-Using a large stripe size can improve performance when accessing very
-large files
-
-Large stripe size allows each client to have exclusive access to its own
-part of a file. However, it can be counterproductive in some cases if it
-does not match your I/O pattern. The choice of stripe size has no effect
-on a single-stripe file.
-
-Read more on
-<http://wiki.lustre.org/manual/LustreManual20_HTML/ManagingStripingFreeSpace.html>
-
-### Lustre on Anselm
-
-The architecture of Lustre on Anselm is composed of two metadata
-servers (MDS) and four data/object storage servers (OSS). Two object
-storage servers are used for file system HOME and another two object
-storage servers are used for file system SCRATCH.
-
- Configuration of the storages
-
-- HOME Lustre object storage
-
-
- - One disk array NetApp E5400
- - 22 OSTs
- - 227 2TB NL-SAS 7.2krpm disks
- - 22 groups of 10 disks in RAID6 (8+2)
- - 7 hot-spare disks
-
-
-
-- SCRATCH Lustre object storage
-
-
- - Two disk arrays NetApp E5400
- - 10 OSTs
- - 106 2TB NL-SAS 7.2krpm disks
- - 10 groups of 10 disks in RAID6 (8+2)
- - 6 hot-spare disks
-
-
-
-- Lustre metadata storage
-
-
- - One disk array NetApp E2600
- - 12 300GB SAS 15krpm disks
- - 2 groups of 5 disks in RAID5
- - 2 hot-spare disks
-
-
-
-###HOME
-
-The HOME filesystem is mounted in directory /home. Users home
-directories /home/username reside on this filesystem. Accessible
-capacity is 320TB, shared among all users. Individual users are
-restricted by filesystem usage quotas, set to 250GB per user. >If
-250GB should prove as insufficient for particular user, please
-contact [support](https://support.it4i.cz/rt),
-the quota may be lifted upon request.
-
-The HOME filesystem is intended for preparation, evaluation, processing
-and storage of data generated by active Projects.
-
-The HOME filesystem should not be used to archive data of past Projects
-or other unrelated data.
-
-The files on HOME filesystem will not be deleted until end of the [users
-lifecycle](../../get-started-with-it4innovations/obtaining-login-credentials/obtaining-login-credentials.html).
-
-The filesystem is backed up, such that it can be restored in case ofÂ
-catasthropic failure resulting in significant data loss. This backup
-however is not intended to restore old versions of user data or to
-restore (accidentaly) deleted files.Â
-
-The HOME filesystem is realized as Lustre parallel filesystem and is
-available on all login and computational nodes.
-Default stripe size is 1MB, stripe count is 1. There are 22 OSTs
-dedicated for the HOME filesystem.
-
-Setting stripe size and stripe count correctly for your needs may
-significantly impact the I/O performance you experience.
-
-HOME filesystem
-Mountpoint
-/home
-Capacity
-320TB
-Throughput
-2GB/s
-User quota
-250GB
-Default stripe size
-1MB
-Default stripe count
-1
-Number of OSTs
-22
-###SCRATCH
-
-The SCRATCH filesystem is mounted in directory /scratch. Users may
-freely create subdirectories and files on the filesystem. Accessible
-capacity is 146TB, shared among all users. Individual users are
-restricted by filesystem usage quotas, set to 100TB per user. The
-purpose of this quota is to prevent runaway programs from filling the
-entire filesystem and deny service to other users. >If 100TB should
-prove as insufficient for particular user, please contact
-[support](https://support.it4i.cz/rt), the quota may be
-lifted upon request.Â
-
-The Scratch filesystem is intended for temporary scratch data generated
-during the calculation as well as for high performance access to input
-and output files. All I/O intensive jobs must use the SCRATCH filesystem
-as their working directory.
-
-Users are advised to save the necessary data from the SCRATCH filesystem
-to HOME filesystem after the calculations and clean up the scratch
-files.
-
-Files on the SCRATCH filesystem that are **not accessed for more than 90
-days** will be automatically **deleted**.
-
-The SCRATCH filesystem is realized as Lustre parallel filesystem and is
-available from all login and computational nodes.
-Default stripe size is 1MB, stripe count is 1. There are 10 OSTs
-dedicated for the SCRATCH filesystem.
-
-Setting stripe size and stripe count correctly for your needs may
-significantly impact the I/O performance you experience.
-
-SCRATCH filesystem
-Mountpoint
-/scratch
-Capacity
-146TB
-Throughput
-6GB/s
-User quota
-100TB
-Default stripe size
-1MB
-Default stripe count
-1
-Number of OSTs
-10
-### Disk usage and quota commands
-
-User quotas on the file systems can be checked and reviewed using
-following command:
-
-`
-$ lfs quota dir
-`
-
-Example for Lustre HOME directory:
-
-`
-$ lfs quota /home
-Disk quotas for user user001 (uid 1234):
- Filesystem kbytes quota limit grace files quota limit grace
- /home 300096 0 250000000 - 2102 0 500000 -
-Disk quotas for group user001 (gid 1234):
- Filesystem kbytes quota limit grace files quota limit grace
- /home 300096 0 0 - 2102 0 0 -
-`
-
-In this example, we view current quota size limit of 250GB and 300MB
-currently used by user001.
-
-Example for Lustre SCRATCH directory:
-
-`
-$ lfs quota /scratch
-Disk quotas for user user001 (uid 1234):
- Filesystem kbytes quota limit grace files quota limit grace
-  /scratch    8    0 100000000000    -    3    0    0    -
-Disk quotas for group user001 (gid 1234):
- Filesystem kbytes quota limit grace files quota limit grace
- /scratch    8    0    0    -    3    0    0    -
-`
-
-In this example, we view current quota size limit of 100TB and 8KB
-currently used by user001.
-
-Â
-
-To have a better understanding of where the space is exactly used, you
-can use following command to find out.
-
-`
-$ du -hs dir
-`
-
-Example for your HOME directory:
-
-`
-$ cd /home
-$ du -hs * .[a-zA-z0-9]* | grep -E "[0-9]*G|[0-9]*M" | sort -hr
-258M cuda-samples
-15M .cache
-13M .mozilla
-5,5M .eclipse
-2,7M .idb_13.0_linux_intel64_app
-`
-
-This will list all directories which are having MegaBytes or GigaBytes
-of consumed space in your actual (in this example HOME) directory. List
-is sorted in descending order from largest to smallest
-files/directories.
-
-To have a better understanding of previous commands, you can read
-manpages.
-
-`
-$ man lfs
-`
-
-`
-$ man du
-`
-
-### Extended ACLs
-
-Extended ACLs provide another security mechanism beside the standard
-POSIX ACLs which are defined by three entries (for
-owner/group/others). Extended ACLs have more than the three basic
-entries. In addition, they also contain a mask entry and may contain any
-number of named user and named group entries.
-
-ACLs on a Lustre file system work exactly like ACLs on any Linux file
-system. They are manipulated with the standard tools in the standard
-manner. Below, we create a directory and allow a specific user access.
-
-`
-[vop999@login1.anselm ~]$ umask 027
-[vop999@login1.anselm ~]$ mkdir test
-[vop999@login1.anselm ~]$ ls -ld test
-drwxr-x--- 2 vop999 vop999 4096 Nov 5 14:17 test
-[vop999@login1.anselm ~]$ getfacl test
-# file: test
-# owner: vop999
-# group: vop999
-user::rwx
-group::r-x
-other::---
-
-[vop999@login1.anselm ~]$ setfacl -m user:johnsm:rwx test
-[vop999@login1.anselm ~]$ ls -ld test
-drwxrwx---+ 2 vop999 vop999 4096 Nov 5 14:17 test
-[vop999@login1.anselm ~]$ getfacl test
-# file: test
-# owner: vop999
-# group: vop999
-user::rwx
-user:johnsm:rwx
-group::r-x
-mask::rwx
-other::---
-`
-
-Default ACL mechanism can be used to replace setuid/setgid permissions
-on directories. Setting a default ACL on a directory (-d flag to
-setfacl) will cause the ACL permissions to be inherited by any newly
-created file or subdirectory within the directory. Refer to this page
-for more information on Linux ACL:
-
-[http://www.vanemery.com/Linux/ACL/POSIX_ACL_on_Linux.html ](http://www.vanemery.com/Linux/ACL/POSIX_ACL_on_Linux.html)
-
-Local Filesystems
------------------
-
-### Local Scratch
-
-Every computational node is equipped with 330GB local scratch disk.
-
-Use local scratch in case you need to access large amount of small files
-during your calculation.
-
-The local scratch disk is mounted as /lscratch and is accessible to
-user at /lscratch/$PBS_JOBID directory.
-
-The local scratch filesystem is intended for temporary scratch data
-generated during the calculation as well as for high performance access
-to input and output files. All I/O intensive jobs that access large
-number of small files within the calculation must use the local scratch
-filesystem as their working directory. This is required for performance
-reasons, as frequent access to number of small files may overload the
-metadata servers (MDS) of the Lustre filesystem.
-
-The local scratch directory /lscratch/$PBS_JOBID will be deleted
-immediately after the calculation end. Users should take care to save
-the output data from within the jobscript.
-
-local SCRATCH filesystem
-Mountpoint
-/lscratch
-Accesspoint
-/lscratch/$PBS_JOBID
-Capacity
-330GB
-Throughput
-100MB/s
-User quota
-none
-### RAM disk
-
-Every computational node is equipped with filesystem realized in memory,
-so called RAM disk.
-
-Use RAM disk in case you need really fast access to your data of limited
-size during your calculation.
-Be very careful, use of RAM disk filesystem is at the expense of
-operational memory.
-
-The local RAM disk is mounted as /ramdisk and is accessible to user
-at /ramdisk/$PBS_JOBID directory.
-
-The local RAM disk filesystem is intended for temporary scratch data
-generated during the calculation as well as for high performance access
-to input and output files. Size of RAM disk filesystem is limited. Be
-very careful, use of RAM disk filesystem is at the expense of
-operational memory. It is not recommended to allocate large amount of
-memory and use large amount of data in RAM disk filesystem at the same
-time.
-
-The local RAM disk directory /ramdisk/$PBS_JOBID will be deleted
-immediately after the calculation end. Users should take care to save
-the output data from within the jobscript.
-
-RAM disk
-Mountpoint
- /ramdisk
-Accesspoint
- /ramdisk/$PBS_JOBID
-Capacity
-60GB at compute nodes without accelerator
-
-90GB at compute nodes with accelerator
-
-500GB at fat nodes
-
-Throughput
-over 1.5 GB/s write, over 5 GB/s read, single thread
-over 10 GB/s write, over 50 GB/s read, 16 threads
-
-User quota
-none
-### tmp
-
-Each node is equipped with local /tmp directory of few GB capacity. The
-/tmp directory should be used to work with small temporary files. Old
-files in /tmp directory are automatically purged.
-
-Summary
-
-----------
-
- Mountpoint Usage Protocol Net Capacity Throughput Limitations Access Services
- ------------------------------------ |---|---|----------------- ---------- ---------------- ------------ ------------- ---------- |**Version**|**Module**|------
- /home home directory Lustre 320 TiB 2 GB/s Quota 250GB Compute and login nodes backed up
- /scratch cluster shared jobs' data Lustre 146 TiB 6 GB/s Quota 100TB Compute and login nodes files older 90 days removed
- /lscratch node local jobs' data local 330 GB 100 MB/s none Compute nodes purged after job ends
- /ramdisk node local jobs' data local 60, 90, 500 GB 5-50 GB/s none Compute nodes purged after job ends
- /tmp local temporary files local 9.5 GB 100 MB/s none Compute and login nodes auto purged
-
-Â
-
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@@ -1,44 +0,0 @@
-Cygwin and X11 forwarding
-=========================
-
-### If no able to forward X11 using PuTTY to CygwinX
-
-`
-[usename@login1.anselm ~]$ gnome-session &
-[1] 23691
-[usename@login1.anselm ~]$ PuTTY X11 proxy: unable to connect to forwarded X server: Network error: Connection refused
-PuTTY X11 proxy: unable to connect to forwarded X server: Network error: Connection refused
-
- (gnome-session:23691): WARNING **: Cannot open display:**
-`
-
- Â
-
-1. Locate and modify
- Cygwin shortcut that
- uses
- Â [startxwin](http://x.cygwin.com/docs/man1/startxwin.1.html)
- locate
- C:cygwin64binXWin.exe
-
-
- change it
- to
- C:*cygwin64binXWin.exe -listen tcp*
-
- 
-
-
-
-2.
- Check Putty settings:
- Enable X11
- forwarding
-
-
-
- 
-
-
-Â
-
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-Graphical User Interface
-========================
-
-
-
-X Window System
----------------
-
-The X Window system is a principal way to get GUI access to the
-clusters.
-
-Read more about configuring [**X Window
-System**](x-window-system/x-window-and-vnc.html).
-
-VNC
----
-
-The **Virtual Network Computing** (**VNC**) is a graphical
-[desktop
-sharing](http://en.wikipedia.org/wiki/Desktop_sharing "Desktop sharing")
-system that uses the [Remote Frame Buffer
-protocol
-(RFB)](http://en.wikipedia.org/wiki/RFB_protocol "RFB protocol")
-to remotely control another
-[computer](http://en.wikipedia.org/wiki/Computer "Computer").
-
-Read more about configuring
-**[VNC](../../../salomon/accessing-the-cluster/graphical-user-interface/vnc.html)**.
-
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-VNC
-===
-
-
-
-The **Virtual Network Computing** (**VNC**) is a graphical [desktop
-sharing](http://en.wikipedia.org/wiki/Desktop_sharing "Desktop sharing")
-system that uses the [Remote Frame Buffer protocol
-(RFB)](http://en.wikipedia.org/wiki/RFB_protocol "RFB protocol") to
-remotely control another
-[computer](http://en.wikipedia.org/wiki/Computer "Computer"). It
-transmits the
-[keyboard](http://en.wikipedia.org/wiki/Computer_keyboard "Computer keyboard")
-and
-[mouse](http://en.wikipedia.org/wiki/Computer_mouse "Computer mouse")
-events from one computer to another, relaying the graphical
-[screen](http://en.wikipedia.org/wiki/Computer_screen "Computer screen")
-updates back in the other direction, over a
-[network](http://en.wikipedia.org/wiki/Computer_network "Computer network").(http://en.wikipedia.org/wiki/Virtual_Network_Computing#cite_note-1)
-
-The recommended clients are
-[TightVNC](http://www.tightvnc.com) or
-[TigerVNC](http://sourceforge.net/apps/mediawiki/tigervnc/index.php?title=Main_Page)
-(free, open source, available for almost any platform).
-
-Create VNC password
--------------------
-
-Local VNC password should be set before the first login. Do use a strong
-password.
-
-`
-[username@login2 ~]$ vncpasswd
-Password:
-Verify:
-`
-
-Start vncserver
----------------
-
-To access VNC a local vncserver must be started first and also a tunnel
-using SSH port forwarding must be established.
-[See below](vnc.html#linux-example-of-creating-a-tunnel)
-for the details on SSH tunnels. In this example we use port 61.
-
-You can find ports which are already occupied. Here you can see that
-ports " /usr/bin/Xvnc :79" and "
-/usr/bin/Xvnc :60" are occupied.
-
-`
-[username@login2 ~]$ ps aux | grep Xvnc
-username   5971 0.0 0.0 201072 92564 ?       SN  Sep22  4:19 /usr/bin/Xvnc :79 -desktop login2:79 (username) -auth /home/gre196/.Xauthority -geometry 1024x768 -rfbwait 30000 -rfbauth /home/username/.vnc/passwd -rfbport 5979 -fp catalogue:/etc/X11/fontpath.d -pn
-username   10296 0.0 0.0 131772 21076 pts/29  SN  13:01  0:01 /usr/bin/Xvnc :60 -desktop login2:61 (username) -auth /home/username/.Xauthority -geometry 1600x900 -depth 16 -rfbwait 30000 -rfbauth /home/jir13/.vnc/passwd -rfbport 5960 -fp catalogue:/etc/X11/fontpath.d -pn
-.....
-`
-
-Choose free port e.g. 61 and start your VNC server:
-
-`
-[username@login2 ~]$ vncserver :61 -geometry 1600x900 -depth 16
-
-New 'login2:1 (username)' desktop is login2:1
-
-Starting applications specified in /home/username/.vnc/xstartup
-Log file is /home/username/.vnc/login2:1.log
-`
-
-Check if VNC server is started on the port (in this example 61):
-
-`
-[username@login2 .vnc]$ vncserver -list
-
-TigerVNC server sessions:
-
-X DISPLAY #Â Â Â Â PROCESS ID
-:61Â Â Â Â Â Â Â Â Â Â Â Â Â 18437
-`
-
-Another command:
-
-`
-[username@login2 .vnc]$ Â ps aux | grep Xvnc
-
-username   10296 0.0 0.0 131772 21076 pts/29  SN  13:01  0:01 /usr/bin/Xvnc :61 -desktop login2:61 (username) -auth /home/jir13/.Xauthority -geometry 1600x900 -depth 16 -rfbwait 30000 -rfbauth /home/username/.vnc/passwd -rfbport 5961 -fp catalogue:/etc/X11/fontpath.d -pn
-`
-
-To access the VNC server you have to create a tunnel between the login
-node using TCP **port 5961** and your machine using a free TCP port (for
-simplicity the very same, in this case).
-
-The tunnel must point to the same login node where you launched the VNC
-server, eg. login2. If you use just cluster-name.it4i.cz, the tunnel
-might point to a different node due to DNS round robin.
-
-###Linux/Mac OS example of creating a tunnel
-
-At your machine, create the tunnel:
-
-`
-local $Â ssh -TN -f username@login2.cluster-name.it4i.cz -L 5961:localhost:5961
-`
-
-Issue the following command to check the tunnel is established (please
-note the PID 2022 in the last column, you'll need it for closing the
-tunnel):
-
-`
-local $ netstat -natp | grep 5961
-(Not all processes could be identified, non-owned process info
-Â will not be shown, you would have to be root to see it all.)
-tcp       0     0 127.0.0.1:5961         0.0.0.0:*              LISTEN     2022/ssh      Â
-tcp6Â Â Â Â Â Â 0Â Â Â Â Â 0 ::1:5961Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â :::*Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â LISTENÂ Â Â Â Â 2022/ssh
-`
-
-Or on Mac OS use this command:
-
-`
-local-mac $ lsof -n -i4TCP:5961 | grep LISTEN
-ssh 75890 sta545 7u IPv4 0xfb062b5c15a56a3b 0t0 TCP 127.0.0.1:5961 (LISTEN)
-`
-
-Connect with the VNC client:
-
-`
-local $ vncviewer 127.0.0.1:5961
-`
-
-In this example, we connect to VNC server on port 5961, via the ssh
-tunnel. The connection is encrypted and secured. The VNC server
-listening on port 5961 provides screen of 1600x900 pixels.
-
-You have to destroy the SSH tunnel which is still running at the
-background after you finish the work. Use the following command (PID
-2022 in this case, see the netstat command above):
-
-`
-kill 2022
-`
-
-### Windows example of creating a tunnel
-
-Use PuTTY to log in on cluster.
-
-Start vncserver using command vncserver described above.
-
-**Search for the localhost and port number (in this case
-127.0.0.1:5961).**
-
-`
-[username@login2 .vnc]$ netstat -tanp | grep Xvnc
-(Not all processes could be identified, non-owned process info
-Â will not be shown, you would have to be root to see it all.)
-tcp       0     0 127.0.0.1:5961             0.0.0.0:*                  LISTEN     24031/Xvnc
-`
-
-On the PuTTY Configuration screen go to Connection->SSH->Tunnels
-to set up the tunnel.
-
-Fill the Source port and Destination fields. **Do not forget to click
-the Add button**.
-
-
-
-Run the VNC client of your choice, select VNC server 127.0.0.1, port
-5961 and connect using VNC password.
-
-### Example of starting TigerVNC viewer
-
-
-
-In this example, we connect to VNC server on port 5961, via the ssh
-tunnel, using TigerVNC viewer. The connection is encrypted and secured.
-The VNC server listening on port 5961 provides screen of 1600x900
-pixels.
-
-### Example of starting TightVNC Viewer
-
-Use your VNC password to log using TightVNC Viewer and start a Gnome
-Session on the login node.
-
-
-
-Gnome session
--------------
-
-You should see after the successful login.
-
-
-
-###Disable your Gnome session screensaver
-
-Open Screensaver preferences dialog:
-
-
-
-Uncheck both options below the slider:
-
-
-
-### Kill screensaver if locked screen
-
-If the screen gets locked you have to kill the screensaver. Do not to
-forget to disable the screensaver then.
-
-`
-[username@login2 .vnc]$ ps aux | grep screen
-username    1503 0.0 0.0 103244  892 pts/4   S+  14:37  0:00 grep screen
-username    24316 0.0 0.0 270564 3528 ?       Ss  14:12  0:00 gnome-screensaver
-
-[username@login2 .vnc]$ kill 24316
-`
-
-### Kill vncserver after finished work
-
-You should kill your VNC server using command:
-
-`
-[username@login2 .vnc]$ vncserver -kill :61
-Killing Xvnc process ID 7074
-Xvnc process ID 7074 already killed
-`
-
-Or this way:
-
-`
-[username@login2 .vnc]$Â pkill vnc
-`
-
-GUI applications on compute nodes over VNC
-------------------------------------------
-
-The very [same methods as described
-above](https://docs.it4i.cz/get-started-with-it4innovations/accessing-the-clusters/graphical-user-interface/x-window-and-vnc#gui-applications-on-compute-nodes),
-may be used to run the GUI applications on compute nodes. However, for
-maximum performance, proceed following these steps:
-
-Open a Terminal (Applications -> System Tools -> Terminal). Run
-all the next commands in the terminal.
-
-
-
-Allow incoming X11 graphics from the compute nodes at the login node:
-
-`
-$ xhost +
-`
-
-Get an interactive session on a compute node (for more detailed info
-[look
-here](../../../../anselm-cluster-documentation/resource-allocation-and-job-execution/job-submission-and-execution.html)).
-Use the **-v DISPLAY** option to propagate the DISPLAY on the compute
-node. In this example, we want a complete node (24 cores in this
-example) from the production queue:
-
-`
-$ qsub -I -v DISPLAY=$(uname -n):$(echo $DISPLAY | cut -d ':' -f 2) -A PROJECT_ID -q qprod -l select=1:ncpus=24
-`
-
-Test that the DISPLAY redirection into your VNC session works, by
-running a X11 application (e. g. XTerm) on the assigned compute node:
-
-`
-$ xterm
-`
-
-Example described above:
-
-
-
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-X Window System
-===============
-
-
-
-The X Window system is a principal way to get GUI access to the
-clusters. The **X Window System** (commonly known as **X11**, based on
-its current major version being 11, or shortened to simply **X**, and
-sometimes informally **X-Windows**) is a computer software system and
-network
-[protocol](http://en.wikipedia.org/wiki/Protocol_%28computing%29 "Protocol (computing)")
-that provides a basis for [graphical user
-interfaces](http://en.wikipedia.org/wiki/Graphical_user_interface "Graphical user interface")
-(GUIs) and rich input device capability for [networked
-computers](http://en.wikipedia.org/wiki/Computer_network "Computer network").
-
-The X display forwarding must be activated and the X server running on
-client side
-
-### X display
-
-In order to display graphical user interface GUI of various software
-tools, you need to enable the X display forwarding. On Linux and Mac,
-log in using the -X option tho ssh client:
-
-`
- local $ ssh -X username@cluster-name.it4i.cz
-`
-
-### X Display Forwarding on Windows
-
-On Windows use the PuTTY client to enable X11 forwarding. Â In PuTTY
-menu, go to Connection->SSH->X11, mark the Enable X11 forwarding
-checkbox before logging in. Then log in as usual.
-
-To verify the forwarding, type
-
-`
-$ echo $DISPLAY
-`
-
-if you receive something like
-
-`
-localhost:10.0
-`
-
-then the X11 forwarding is enabled.
-
-### X Server
-
-In order to display graphical user interface GUI of various software
-tools, you need running X server on your desktop computer. For Linux
-users, no action is required as the X server is the default GUI
-environment on most Linux distributions. Mac and Windows users need to
-install and run the X server on their workstations.
-
-### X Server on OS X
-
-Mac OS users need to install [XQuartz
-server](http://xquartz.macosforge.org/landing/).
-
-### X Server on Windows
-
-There are variety of X servers available for Windows environment. The
-commercial Xwin32 is very stable and rich featured. The Cygwin
-environment provides fully featured open-source XWin X server. For
-simplicity, we recommend open-source X server by the [Xming
-project](http://sourceforge.net/projects/xming/). For
-stability and full features we recommend the
-[XWin](http://x.cygwin.com/) X server by Cygwin
-
- |How to use Xwin |How to use Xming |
- | --- | --- |
- |[Install Cygwin](http://x.cygwin.com/)Find and execute XWin.exeto start the X server on Windows desktop computer.[If no able to forward X11 using PuTTY to CygwinX](x-window-system/cygwin-and-x11-forwarding.html)\ |<p>Use Xlaunch to configure the Xming.<p>Run Xmingto start the X server on Windows desktop computer.\ |
-
-Read more on
-[http://www.math.umn.edu/systems_guide/putty_xwin32.html](http://www.math.umn.edu/systems_guide/putty_xwin32.shtml)
-
-### Running GUI Enabled Applications
-
-Make sure that X forwarding is activated and the X server is running.
-
-Then launch the application as usual. Use the & to run the application
-in background.
-
-`
-$ module load intel (idb and gvim not installed yet)
-$ gvim &
-`
-
-`
-$ xterm
-`
-
-In this example, we activate the intel programing environment tools,
-then start the graphical gvim editor.
-
-### GUI Applications on Compute Nodes
-
-Allocate the compute nodes using -X option on the qsub command
-
-`
-$ qsub -q qexp -l select=2:ncpus=24 -X -I
-`
-
-In this example, we allocate 2 nodes via qexp queue, interactively. We
-request X11 forwarding with the -X option. It will be possible to run
-the GUI enabled applications directly on the first compute node.
-
-**Better performance** is obtained by logging on the allocated compute
-node via ssh, using the -X option.
-
-`
-$ ssh -X r24u35n680
-`
-
-In this example, we log in on the r24u35n680 compute node, with the X11
-forwarding enabled.
-
-HTML commented section #1 (no GUI on Compute nodes - Xvfb)
-
-### The Gnome GUI Environment
-
-The Gnome 2.28 GUI environment is available on the clusters. We
-recommend to use separate X server window for displaying the Gnome
-environment.
-
-### Gnome on Linux and OS X
-
-To run the remote Gnome session in a window on Linux/OS X computer, you
-need to install Xephyr. Ubuntu package is
-xserver-xephyr, on OS X it is part of
-[XQuartz](http://xquartz.macosforge.org/landing/).
-First, launch Xephyr on local machine:
-
-`
-local $ Xephyr -ac -screen 1024x768 -br -reset -terminate :1 &
-`
-
-This will open a new X window with size 1024x768 at DISPLAY :1. Next,
-ssh to the cluster with DISPLAY environment variable set and launch
- gnome-session
-
- local $ DISPLAY=:1.0 ssh -XC yourname@cluster-name.it4i.cz -i ~/.ssh/path_to_your_key
- ... cluster-name MOTD...
- yourname@login1.cluster-namen.it4i.cz $Â gnome-session &
-
-On older systems where Xephyr is not available, you may also try Xnest
-instead of Xephyr. Another option is to launch a new X server in a
-separate console, via:
-
-`
-xinit /usr/bin/ssh -XT -i .ssh/path_to_your_key yourname@cluster-namen.it4i.cz gnome-session -- :1 vt12
-`
-
-However this method does not seem to work with recent Linux
-distributions and you will need to manually source
-/etc/profile to properly set environment
-variables for PBS.
-
-### Gnome on Windows
-
-Use Xlaunch to start the Xming server or run the XWin.exe. Select the
-''One window" mode.
-
-Log in to the cluster, using PuTTY. On the cluster, run the
-gnome-session command.
-
-`
-$ gnome-session &
-`
-
-In this way, we run remote gnome session on the cluster, displaying it
-in the local X server
-
-Use System->Log Out to close the gnome-session
-
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-Accessing the Clusters
-======================
-
-The IT4Innovations clusters are accessed by SSH protocol via login
-nodes.
-
-Read more on [Accessing the Salomon
-Cluste](../salomon/accessing-the-cluster.html)r or
-[Accessing the Anselm
-Cluster](../anselm-cluster-documentation/accessing-the-cluster.html)
-pages.
-
-### PuTTY
-
-On **Windows**, use [PuTTY ssh
-client](accessing-the-clusters/shell-access-and-data-transfer/putty/putty.html).
-
-### SSH keys
-
-Read more about [SSH keys
-management](accessing-the-clusters/shell-access-and-data-transfer/ssh-keys.html).
-
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-Accessing the Clusters
-======================
-
-The IT4Innovations clusters are accessed by SSH protocol via login
-nodes.
-
-Read more on [Accessing the Salomon
-Cluste](../../../salomon/accessing-the-cluster.html)r or
-[Accessing the Anselm
-Cluster](../../../anselm-cluster-documentation/accessing-the-cluster.html)
-pages.
-
-### PuTTY
-
-On **Windows**, use [PuTTY ssh
-client](putty/putty.html).
-
-### SSH keys
-
-Read more about [SSH keys management](ssh-keys.html).
-
diff --git a/converted/docs.it4i.cz/get-started-with-it4innovations/accessing-the-clusters/shell-access-and-data-transfer/introduction.pdf b/converted/docs.it4i.cz/get-started-with-it4innovations/accessing-the-clusters/shell-access-and-data-transfer/introduction.pdf
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-Pageant SSH agent
-=================
-
-
-
-Pageant holds your private key in memory without needing to retype a
-passphrase on every login.
-
-- Run Pageant.
-- On Pageant Key List press *Add key* and select your private
- key (id_rsa.ppk).
-- Enter your passphrase.
-- Now you have your private key in memory without needing to retype a
- passphrase on every login.
-
- 
-
-Â
-
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+++ /dev/null
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-PuTTY
-=====
-
-
-
-PuTTY - before we start SSH connection
----------------------------------------------------------------------------------
-
-### Windows PuTTY Installer
-
-We recommned you to download "**A Windows installer for everything
-except PuTTYtel**" with **Pageant*** (SSH authentication agent) and
-**PuTTYgen** (PuTTY key generator) which is available
-[here](http://www.chiark.greenend.org.uk/~sgtatham/putty/download.html).
-
- After installation you can proceed directly
-to private keys authentication using
-["Putty"](putty.html#putty).
-"Change Password for Existing Private Key" is optional.
-"Generate a New Public/Private key (`id_rsa/id_rsa.ppk` ): `600 (-rw-------)` pair" is intended for users without
-Public/Private key (`id_rsa/id_rsa.ppk` ): `600 (-rw-------)` in the initial email containing login credentials.
-"Pageant" is optional.
-
-### PuTTYgen
-
-PuTTYgen is the PuTTY key generator. Read more how to load in an
-existing private key and change your passphrase or generate a new
-public/private key pair using [PuTTYgen](puttygen.html)
-if needed.
-
-### Pageant SSH agent
-
-[Pageant](pageant.html) holds your private key in memory
-without needing to retype a passphrase on every login. We recommend its
-usage.
-
-PuTTY - how to connect to the IT4Innovations cluster
---------------------------------------------------------
-
-- Run PuTTY
-- Enter Host name and Save session fields with [Login
- address](../../../../salomon/accessing-the-cluster/shell-and-data-access/shell-and-data-access.html)
- and browse Connection - > SSH -> Auth menu.
- The *Host Name* input may be in the format
- **"username@clustername.it4i.cz"** so you don't have to type your
- login each time.
- In this example we will connect to the Salomon cluster using
- Â **"salomon.it4i.cz"**.
-
- 
-
-Â
-
-- Category -> Connection - > SSH -> Auth:
- Select Attempt authentication using Pageant.
- Select Allow agent forwarding.
- Browse and select your [private
- key](../ssh-keys.html) file.
-
- 
-
-- Return to Session page and Save selected configuration with *Save*
- button.
-
- 
-
-- Now you can log in using *Open* button.
-
- 
-
-- Enter your username if the *Host Name* input is not in the format
- "username@salomon.it4i.cz".
-
-- Enter passphrase for selected [private
- key](../ssh-keys.html) file if Pageant **SSH
- authentication agent is not used.**
-
-
-Another PuTTY Settings
-----------------------
-
-- Category -> Windows -> Translation -> Remote character set
- and select **UTF-8**.
-
-- Category -> Terminal -> Features and select **Disable
- application keypad mode** (enable numpad)
-- Save your configuration on Session page in to Default Settings with
- *Save* button .
-
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-PuTTY key generator
-===================
-
-
-
-PuTTYgen is the PuTTY key generator. You can load in an existing private
-key and change your passphrase or generate a new public/private key
-pair.
-
-### Change Password for Existing Private Key
-
-You can change the password of your SSH key with "PuTTY Key Generator".
-Make sure to backup the key.
-
-- Load your [private key](../ssh-keys.html) file with
- *Load* button.
-- Enter your current passphrase.
-- Change key passphrase.
-- Confirm key passphrase.
-- Save your private key with *Save private key* button.
-
- 
-
-Â
-
-### Generate a New Public/Private key (`id_rsa/id_rsa.ppk` ): `600 (-rw-------)` pair
-
-You can generate an additional public/private key pair and insert public
-key into authorized_keys file for authentication with your own private
-key.
-
-- Start with *Generate* button.
-
- 
-
-- Generate some randomness.
-
- 
-
-- Wait.
-
- 
-
-- Enter a *comment* for your key using formatÂ
- 'username@organization.example.com'.
- Enter key passphrase.
- Confirm key passphrase.
- Save your new private key `in "*.ppk" `format with *Save private
- key* button.
-
- 
-
-- Save the public key with *Save public key* button.
- You can copy public key out of the â€Public key for pasting into
- authorized_keys file’ box.
-
- 
-
-- Export private key in OpenSSH format "id_rsa" using Conversion
- -> Export OpenSSH key
-
- 
-
-- Now you can insert additional public key into authorized_keys file
- for authentication with your own private key.
- You must log in using ssh key received after registration. Then
- proceed to [How to add your own
- key](../ssh-keys.html).
-
-
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+++ /dev/null
@@ -1,146 +0,0 @@
-SSH keys
-========
-
-
-
- Key management
--------------------------------------------------------------------
-
-After logging in, you can see .ssh/ directory with SSH keys and
-authorized_keys file:
-
- $ cd /home/username/
- $ ls -la .ssh/
- total 24
- drwx------ 2 username username 4096 May 13 15:12 .
- drwxr-x---22 username username 4096 May 13 07:22 ..
- -rw-r--r-- 1 username username 392 May 21 2014 authorized_keys
- -rw------- 1 username username 1675 May 21Â 2014 id_rsa
- -rw------- 1 username username 1460 May 21Â 2014 id_rsa.ppk
- -rw-r--r-- 1 username username 392 May 21 2014 id_rsa.pub
-
- Please note that private keys in
-.ssh directory are without passphrase and allow you to connect within
-the cluster.
-
-### Access privileges on .ssh folder
-
-- `.ssh` Â directory: 700 (drwx------)
-
- Â directory:
- 700 (drwx------)
--
- Authorized_keys, known_hosts and public key (`.pub` file): `644 (-rw-r--r--)`Â
-
-
- known_hosts and
- public keyÂ
- (`.pub`
-
- Â file):Â
-
- `644 (-rw-r--r--)`
--
- ``
-
- Private key (`id_rsa/id_rsa.ppk` ): `600 (-rw-------)`
- (`id_rsa/id_rsa.ppk`
- ):
- `600 (-rw-------)`
-
-
-
- cd /home/username/
- chmod 700 .ssh/
- chmod 644 .ssh/authorized_keys
- chmod 644 .ssh/id_rsa.pub
- chmod 644 .ssh/known_hosts
- chmod 600 .ssh/id_rsa
- chmod 600Â .ssh/id_rsa.ppk
-
-Private key (`id_rsa/id_rsa.ppk` ): `600 (-rw-------)`
------------
-
-The path to a private key is usually /home/username/.ssh/
-
-Private key (`id_rsa/id_rsa.ppk` ): `600 (-rw-------)` file in "id_rsa" or `"*.ppk" `format is used to
-authenticate with the servers.
-Private key (`id_rsa/id_rsa.ppk` ): `600 (-rw-------)` is present locally
-on local side and used for example in SSH agent [Pageant (for Windows
-users)](putty/PageantV.png). The private key should
-always be kept in a safe place.
-
- An example of private key
-format:
-
- -----BEGIN RSA PRIVATE KEY-----
- MIIEpAIBAAKCAQEAqbo7jokygnBpG2wYa5NB45ns6+UKTNLMLHF0BO3zmRtKEElE
- aGqXfbYwvXlcuRb2d9/Y5dVpCZHV0kbY3NhtVOcEIe+1ROaiU9BEsUAhMNEvgiLV
- gSql4QvRO4BWPlM8+WAWXDp3oeoBh8glXyuh9teb8yq98fv1r1peYGRrW3/s4V+q
- O1SQ0XY2T7rWCYRLIP6rTMXArTI35v3WU513mn7nm1fJ7oN0QgVH5b0W9V1Kyc4l
- 9vILHeMXxvz+i/5jTEfLOJpiRGYZYcaYrE4dIiHPl3IlbV7hlkK23Xb1US8QJr5G
- ADxp1VTkHjY+mKagEfxl1hQIb42JLHhKMEGqNQIDAQABAoIBAQCkypPuxZjL+vai
- UGa5dAWiRZ46P2yrwHPKpvEdpCdDPbLAc1K/CtdBkHZsUPxNHVV6eFWweW99giIY
- Av+mFWC58X8asBHQ7xkmxW0cqAZRzpkRAl9IBS9/fKjO28Fgy/p+suOi8oWbKIgJ
- 3LMkX0nnT9oz1AkOfTNC6Tv+3SE7eTj1RPcMjur4W1Cd1N3EljLszdVk4tLxlXBS
- yl9NzVnJJbJR4t01l45VfFECgYEAno1WJSB/SwdZvS9GkfhvmZd3r4vyV9Bmo3dn
- XZAh8HRW13imOnpklDR4FRe98D9A7V3yh9h60Co4oAUd6N+Oc68/qnv/8O9efA+M
- /neI9ANYFo8F0+yFCp4Duj7zPV3aWlN/pd8TNzLqecqh10uZNMy8rAjCxybeZjWd
- DyhgywXhAoGBAN3BCazNefYpLbpBQzwes+f2oStvwOYKDqySWsYVXeVgUI+OWTVZ
- eZ26Y86E8MQO+q0TIxpwou+TEaUgOSqCX40Q37rGSl9K+rjnboJBYNCmwVp9bfyj
- kCLL/3g57nTSqhgHNa1xwemePvgNdn6FZteA8sXiCg5ZzaISqWAffek5AoGBAMPw
- V/vwQ96C8E3l1cH5cUbmBCCcfXM2GLv74bb1V3SvCiAKgOrZ8gEgUiQ0+TfcbAbe
- 7MM20vRNQjaLTBpai/BTbmqM1Q+r1KNjq8k5bfTdAoGANgzlNM9omM10rd9WagL5
- yuJcal/03p048mtB4OI4Xr5ZJISHze8fK4jQ5veUT9Vu2Fy/w6QMsuRf+qWeCXR5
- RPC2H0JzkS+2uZp8BOHk1iDPqbxWXJE9I57CxBV9C/tfzo2IhtOOcuJ4LY+sw+y/
- ocKpJbdLTWrTLdqLHwicdn8OxeWot1mOukyK2l0UeDkY6H5pYPtHTpAZvRBd7ETL
- Zs2RP3KFFvho6aIDGrY0wee740/jWotx7fbxxKwPyDRsbH3+1Wx/eX2RND4OGdkH
- gejJEzpk/7y/P/hCad7bSDdHZwO+Z03HIRC0E8yQz+JYatrqckaRCtd7cXryTmTR
- FbvLJmECgYBDpfno2CzcFJCTdNBZFi34oJRiDb+HdESXepk58PcNcgK3R8PXf+au
- OqDBtZIuFv9U1WAg0gzGwt/0Y9u2c8m0nXziUS6AePxy5sBHs7g9C9WeZRz/nCWK
- +cHIm7XOwBEzDKz5f9eBqRGipm0skDZNKl8X/5QMTT5K3Eci2n+lTw==
- -----END RSA PRIVATE KEY-----
-
-Public key
-----------
-
-Public key file in "*.pub" format is used to
-verify a
-Â
-digital signature. Public
-key is present on the remote
-side and allows access to
-the owner of the matching private key.
-
- An example of public key
-format:
-
- ssh-rsa AAAAB3NzaC1yc2EAAAADAQABAAABAQCpujuOiTKCcGkbbBhrk0Hjmezr5QpM0swscXQE7fOZG0oQSURoapd9tjC9eVy5FvZ339jl1WkJkdXSRtjc2G1U5wQh77VE5qJT0ESxQCEw0S+CItWBKqXhC9E7gFY+UyP5YBZcOneh6gGHyCVfK6H215vzKr3x+/WvWl5gZGtbf+zhX6o4RJDRdjZPutYJhEsg/qtMxcCtMjfm/dZTnXeafuebV8nug3RCBUflvRb1XUrJuiX28gsd4xfG/P6L/mNMR8s4kmJEZhlhxpj8Th0iIc+XciVtXuGWQrbddcVRLxAmvkYAPGnVVOQeNj69pqAR/GXaFAhvjYkseEowQao1 username@organization.example.com
-
-### How to add your own key
-
-First, generate a new keypair of your public and private key:
-
- local $ ssh-keygen -C 'username@organization.example.com' -f additional_key
-
-Please, enter **strong** **passphrase** for securing your private key.
-
-You can insert additional public key into authorized_keys file for
-authentication with your own private key. Additional records in
-authorized_keys file must be delimited by new line. Users are
-not advised to remove the default public key from authorized_keys file.
-
-Example:
-
- $ cat additional_key.pub > ~/.ssh/authorized_keys
-
-In this example, we add an additional public key, stored in file
-additional_key.pub into the authorized_keys. Next time we log in, we
-will be able to use the private addtional_key key to log in.
-
-### How to remove your own key
-
-Removing your key from authorized_keys can be done simply by deleting
-the corresponding public key which can be identified by a comment at the
-end of line (eg. username@organization.example.com).
-
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+++ /dev/null
@@ -1,27 +0,0 @@
-VPN - Connection fail in Win 8.1
-================================
-
-**Failed to initialize connection subsystem Win 8.1 - 02-10-15 MS patch
-
-AnyConnect users on Windows 8.1 will receive a "Failed to initialize
-connection subsystem" error after installing the Windows 8.1 02/10/15
-security patch. This OS defect introduced with the 02/10/15 patch update
-will also impact WIndows 7 users with IE11. Windows Server
-2008/2012 are also impacted by this defect, but neither is a supported
-OS for AnyConnect.
-
-**Workaround:**
-
-- Close the Cisco AnyConnect Window and the taskbar mini-icon
-- Right click vpnui.exe in the 'Cisco AnyConnect Secure Mobility
- Client' folder. (C:Program Files (x86)CiscoCisco AnyConnect
- Secure Mobility
-- Client)
-- Click on the 'Run compatibility troubleshooter' button
-- Choose 'Try recommended settings'
-- The wizard suggests Windows 8 compatibility.
-- Click 'Test Program'. This will open the program.
-- Close
-
-
-
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+++ /dev/null
@@ -1,38 +0,0 @@
-Applying for Resources
-======================
-
-
-
-Computational resources may be allocated by any of the following
-[Computing resources
-allocation](http://www.it4i.cz/computing-resources-allocation/?lang=en)
-mechanisms.
-
-Academic researchers can apply for computational resources via [Open
-Access
-Competitions](http://www.it4i.cz/open-access-competition/?lang=en&lang=en).
-
-Anyone is welcomed to apply via the [Directors
-Discretion.](http://www.it4i.cz/obtaining-computational-resources-through-directors-discretion/?lang=en&lang=en)
-
-Foreign (mostly European) users can obtain computational resources via
-the [PRACE (DECI)
-program](http://www.prace-ri.eu/DECI-Projects).
-
-In all cases, IT4Innovations’ access mechanisms are aimed at
-distributing computational resources while taking into account the
-development and application of supercomputing methods and their benefits
-and usefulness for society. The applicants are expected to submit a
-proposal. In the proposal, the applicants **apply for a particular
-amount of core-hours** of computational resources. The requested
-core-hours should be substantiated by scientific excellence of the
-proposal, its computational maturity and expected impacts.
-Proposals do undergo a scientific, technical and economic
-evaluation. The allocation decisions are based on this
-evaluation. More information at [Computing resources
-allocation](http://www.it4i.cz/computing-resources-allocation/?lang=en)
-and [Obtaining Login
-Credentials](obtaining-login-credentials.html) page.
-
-Â
-
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+++ /dev/null
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-Certificates FAQ
-================
-
-FAQ about certificates in general
-
-
-
-Q: What are certificates?
--------------------------
-
-IT4Innovations employs X.509 certificates for secure communication (e.
-g. credentials exchange) and for grid services related to PRACE, as they
-present a single method of authentication for all PRACE services, where
-only one password is required.
-
-There are different kinds of certificates, each with a different scope
-of use. We mention here:
-
-- User (Private) certificates
-
-- Certificate Authority (CA) certificates
-
-- Host certificates
-
-- Service certificates
-
-Â
-
-**However, users need only manage User and CA certificates. Note that your
-user certificate is protected by an associated private key, and this
-private key must never be disclosed**.
-
-Q: Which X.509 certificates are recognised by IT4Innovations?
--------------------------------------------------------------
-
-Any certificate that has been issued by a Certification Authority (CA)
-from a member of the IGTF ([http:www.igtf.net](http://www.igtf.net/)) is
-recognised by IT4Innovations: European certificates are issued by
-members of the EUGridPMA
-([https://www.eugridmpa.org](https://www.eugridpma.org/)), which is part
-of the IGTF and coordinates the trust fabric for e-Science Grid
-authentication within Europe. Further the Czech *"Qualified certificate"
-(Kvalifikovaný certifikát)* (provided by <http://www.postsignum.cz/> or
-<http://www.ica.cz/Kvalifikovany-certifikat.aspx>), that is used in
-electronic contact with Czech public authorities is accepted.
-
-Q: How do I get a User Certificate that can be used with IT4Innovations?
-------------------------------------------------------------------------
-
-To get a certificate, you must make a request to your local, IGTF
-approved, Certificate Authority (CA). Usually you then must visit, in
-person, your nearest Registration Authority (RA) to verify your
-affiliation and identity (photo identification is required). Usually,
-you will then be emailed details on how to retrieve your certificate,
-although procedures can vary between CAs. If you are in Europe, you can
-locate your trusted CA via <http://www.eugridpma.org/members/worldmap>.
-
-In some countries certificates can also be retrieved using the TERENA
-Certificate Service, see the FAQ below for the link.
-
-Q: Does IT4Innovations support short lived certificates (SLCS)?
----------------------------------------------------------------
-
-Yes, provided that the CA which provides this service is also a member
-of IGTF.
-
-Q: Does IT4Innovations support the TERENA certificate service?
---------------------------------------------------------------
-
-Yes, ITInnovations supports TERENA eScience personal certificates. For
-more information, please visit
-[https://tcs-escience-portal.terena.org](https://tcs-escience-portal.terena.org/){.spip_url
-.spip_out}, where you also can find if your organisation/country can use
-this service
-
-Q: What format should my certificate take?
-------------------------------------------
-
-User Certificates come in many formats, the three most common being the
-’PKCS12’, ’PEM’ and the JKS formats.
-
-The PKCS12 (often abbreviated to ’p12’) format stores your user
-certificate, along with your associated private key, in a single file.
-This form of your certificate is typically employed by web browsers,
-mail clients, and grid services like UNICORE, DART, gsissh-term and
-Globus toolkit (GSI-SSH, GridFTP and GRAM5).
-
-The PEM format (*.pem) stores your user certificate and your associated
-private key in two separate files. This form of your certificate can be
-used by PRACE’s gsissh-term and with the grid related services like
-Globus toolkit (GSI-SSH, GridFTP and GRAM5).
-
-To convert your Certificate from PEM to p12 formats, and *vice versa*,
-IT4Innovations recommends using the openssl tool (see separate FAQ
-entry).
-
-JKS is the Java KeyStore and may contain both your personal certificate
-with your private key and a list of your trusted CA certificates. This
-form of your certificate can be used by grid services like DART and
-UNICORE6.
-
-To convert your Certificate from p12 to JKS, IT4Innovations recommends
-using the keytool utiliy (see separate FAQ entry).
-
-Q: What are CA certificates?
-----------------------------
-
-Certification Authority (CA) certificates are used to verify the link
-between your user certificate and the authority which issued it. They
-are also used to verify the link between the host certificate of a
-IT4Innovations server and the CA which issued that certificate. In
-essence they establish a chain of trust between you and the target
-server. Thus, for some grid services, users must have a copy of all the
-CA certificates.
-
-To assist users, SURFsara (a member of PRACE) provides a complete and
-up-to-date bundle of all the CA certificates that any PRACE user (or
-IT4Innovations grid services user) will require. Bundle of certificates,
-in either p12, PEM or JKS formats, are available from
-<http://winnetou.sara.nl/prace/certs/>.
-
-It is worth noting that gsissh-term and DART automatically updates their
-CA certificates from this SURFsara website. In other cases, if you
-receive a warning that a server’s certificate can not be validated (not
-trusted), then please update your CA certificates via the SURFsara
-website. If this fails, then please contact the IT4Innovations helpdesk.
-
-Lastly, if you need the CA certificates for a personal Globus 5
-installation, then you can install the CA certificates from a MyProxy
-server with the following command.
-
- myproxy-get-trustroots -s myproxy-prace.lrz.de
-
-If you run this command as ’root’, then it will install the certificates
-into /etc/grid-security/certificates. If you run this not as ’root’,
-then the certificates will be installed into
-$HOME/.globus/certificates. For Globus, you can download the
-globuscerts.tar.gz packet from <http://winnetou.sara.nl/prace/certs/>.
-
-Q: What is a DN and how do I find mine?
----------------------------------------
-
-DN stands for Distinguished Name and is part of your user certificate.
-IT4Innovations needs to know your DN to enable your account to use the
-grid services. You may use openssl (see below) to determine your DN or,
-if your browser contains your user certificate, you can extract your DN
-from your browser.
-
-For Internet Explorer users, the DN is referred to as the "subject" of
-your certificate. Tools->Internet
-Options->Content->Certificates->View->Details->Subject.
-
-For users running Firefox under Windows, the DN is referred to as the
-"subject" of your certificate.
-Tools->Options->Advanced->Encryption->View Certificates.
-Highlight your name and then Click View->Details->Subject.
-
-Q: How do I use the openssl tool?
----------------------------------
-
-The following examples are for Unix/Linux operating systems only.
-
-To convert from PEM to p12, enter the following command:
-
- openssl pkcs12 -export -in usercert.pem -inkey userkey.pem -out
- username.p12
-
-To convert from p12 to PEM, type the following *four* commands:
-
- openssl pkcs12 -in username.p12 -out usercert.pem -clcerts -nokeys
- openssl pkcs12 -in username.p12 -out userkey.pem -nocerts
- chmod 444 usercert.pem
- chmod 400 userkey.pem
-
-To check your Distinguished Name (DN), enter the following command:
-
- openssl x509 -in usercert.pem -noout -subject -nameopt
- RFC2253
-
-To check your certificate (e.g., DN, validity, issuer, public key
-algorithm, etc.), enter the following command:
-
- openssl x509 -in usercert.pem -text -noout
-
-To download openssl for both Linux and Windows, please visit
-<http://www.openssl.org/related/binaries.html>. On Macintosh Mac OS X
-computers openssl is already pre-installed and can be used immediately.
-
-Q: How do I create and then manage a keystore?
-----------------------------------------------
-
-IT4innovations recommends the java based keytool utility to create and
-manage keystores, which themselves are stores of keys and certificates.
-For example if you want to convert your pkcs12 formatted key pair into a
-java keystore you can use the following command.
-
- keytool -importkeystore -srckeystore $my_p12_cert -destkeystore
- $my_keystore -srcstoretype pkcs12 -deststoretype jks -alias
- $my_nickname -destalias $my_nickname
-
-where $my_p12_cert is the name of your p12 (pkcs12) certificate,
-$my_keystore is the name that you give to your new java keystore and
-$my_nickname is the alias name that the p12 certificate was given and
-is used also for the new keystore.
-
-You also can import CA certificates into your java keystore with the
-tool, e.g.:
-
- keytool -import -trustcacerts -alias $mydomain -file $mydomain.crt -keystore $my_keystore
-
-where $mydomain.crt is the certificate of a trusted signing authority
-(CA) and $mydomain is the alias name that you give to the entry.
-
-More information on the tool can be found
-at:<http://docs.oracle.com/javase/7/docs/technotes/tools/solaris/keytool.html>
-
-Q: How do I use my certificate to access the different grid Services?
----------------------------------------------------------------------
-
-Most grid services require the use of your certificate; however, the
-format of your certificate depends on the grid Service you wish to
-employ.
-
-If employing the PRACE version of GSISSH-term (also a Java Web Start
-Application), you may use either the PEM or p12 formats. Note that this
-service automatically installs up-to-date PRACE CA certificates.
-
-If the grid service is UNICORE, then you bind your certificate, in
-either the p12 format or JKS, to UNICORE during the installation of the
-client on your local machine. For more information, please visit
-[UNICORE6 in PRACE](http://www.prace-ri.eu/UNICORE6-in-PRACE)
-
-If the grid service is part of Globus, such as GSI-SSH, GriFTP or GRAM5,
-then the certificates can be in either p12 or PEM format and must reside
-in the "$HOME/.globus" directory for Linux and Mac users or
-%HOMEPATH%.globus for Windows users. (Windows users will have to use
-the DOS command ’cmd’ to create a directory which starts with a ’.’).
-Further, user certificates should be named either "usercred.p12" or
-"usercert.pem" and "userkey.pem", and the CA certificates must be kept
-in a pre-specified directory as follows. For Linux and Mac users, this
-directory is either $HOME/.globus/certificates or
-/etc/grid-security/certificates. For Windows users, this directory is
-%HOMEPATH%.globuscertificates. (If you are using GSISSH-Term from
-prace-ri.eu then you do not have to create the .globus directory nor
-install CA certificates to use this tool alone).
-
-Q: How do I manually import my certificate into my browser?
------------------------------------------------------------
-
-If you employ the Firefox browser, then you can import your certificate
-by first choosing the "Preferences" window. For Windows, this is
-Tools->Options. For Linux, this is Edit->Preferences. For Mac,
-this is Firefox->Preferences. Then, choose the "Advanced" button;
-followed by the "Encryption" tab. Then, choose the "Certificates" panel;
-select the option "Select one automatically" if you have only one
-certificate, or "Ask me every time" if you have more then one. Then
-click on the "View Certificates" button to open the "Certificate
-Manager" window. You can then select the "Your Certificates" tab and
-click on button "Import". Then locate the PKCS12 (.p12) certificate you
-wish to import, and employ its associated password.
-
-If you are a Safari user, then simply open the "Keychain Access"
-application and follow "File->Import items".
-
-If you are an Internet Explorer user, click
-Start->Settings->Control Panel and then double-click on Internet.
-On the Content tab, click Personal, and then click Import. In the
-Password box, type your password. NB you may be prompted multiple times
-for your password. In the "Certificate File To Import" box, type the
-filename of the certificate you wish to import, and then click OK. Click
-Close, and then click OK.
-
-Q: What is a proxy certificate?
--------------------------------
-
-A proxy certificate is a short-lived certificate which may be employed
-by UNICORE and the Globus services. The proxy certificate consists of a
-new user certificate and a newly generated proxy private key. This proxy
-typically has a rather short lifetime (normally 12 hours) and often only
-allows a limited delegation of rights. Its default location, for
-Unix/Linux, is /tmp/x509_u*uid* but can be set via the
-$X509_USER_PROXY environment variable.
-
-Q: What is the MyProxy service?
--------------------------------
-
-[The MyProxy Service](http://grid.ncsa.illinois.edu/myproxy/)
-, can be employed by gsissh-term and Globus tools, and is
-an online repository that allows users to store long lived proxy
-certificates remotely, which can then be retrieved for use at a later
-date. Each proxy is protected by a password provided by the user at the
-time of storage. This is beneficial to Globus users as they do not have
-to carry their private keys and certificates when travelling; nor do
-users have to install private keys and certificates on possibly insecure
-computers.
-
-Q: Someone may have copied or had access to the private key of my certificate either in a separate file or in the browser. What should I do?
---------------------------------------------------------------------------------------------------------------------------------------------
-
-Please ask the CA that issued your certificate to revoke this certifcate
-and to supply you with a new one. In addition, please report this to
-IT4Innovations by contacting [the support
-team](https://support.it4i.cz/rt).
-
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-Obtaining Login Credentials
-===========================
-
-
-
-Obtaining Authorization
------------------------
-
-The computational resources of IT4I Â are allocated by the Allocation
-Committee to a [Project](../introduction.html),
-investigated by a Primary Investigator. By allocating the computational
-resources, the Allocation Committee is authorizing the PI to access and
-use the clusters. The PI may decide to authorize a number of her/his
-Collaborators to access and use the clusters, to consume the resources
-allocated to her/his Project. These collaborators will be associated to
-the Project. The Figure below is depicting the authorization chain:
-
-
-
-Â You need to either [become the
-PI](../applying-for-resources.html) or [be named as a
-collaborator](obtaining-login-credentials.html#authorization-of-collaborator-by-pi)
-by a PI in order to access and use the clusters.
-
-Head of Supercomputing Services acts as a PI of a project DD-13-5.
-Joining this project, you may **access and explore the clusters**, use
-software, development environment and computers via the qexp and qfree
-queues. You may use these resources for own education/research, no
-paperwork is required. All IT4I employees may contact the Head of
-Supercomputing Services in order to obtain **free access to the
-clusters**.
-
-### Authorization of PI by Allocation Committee
-
-The PI is authorized to use the clusters by the allocation decision
-issued by the Allocation Committee.The PI will be informed by IT4I about
-the Allocation Committee decision.
-
-### Authorization by web
-
-This is a preferred way of granting access to project resources.
-Please, use this method whenever it's possible.
-
-Log in to the [IT4I Extranet
-portal](https://extranet.it4i.cz) using IT4I credentials
-and go to the **Projects** section.
-
-- **Users:** Please, submit your requests for becoming a
- project member.
-- **Primary Investigators:** Please, approve or deny users' requests
- in the same section.
-
-### Authorization by e-mail (an alternative approach)
-
-Â In order to authorize a Collaborator to utilize the allocated
-resources, the PI should contact the [IT4I
-support](https://support.it4i.cz/rt/) (E-mail: [support
-[at] it4i.cz](mailto:support%20%5Bat%5D%20it4i.cz)) and provide
-following information:
-
-1. Identify your project by project ID
-2. Provide list of people, including himself, who are authorized to use
- the resources allocated to the project. The list must include full
- name, e-mail and affiliation. Provide usernames as well, ifÂ
- collaborator login access already exists on the IT4I systems.
-3. Include "Authorization to IT4Innovations" into the subject line.
-
-Example (except the subject line which must be in English, you may use
-Czech or Slovak language for communication with us):
-
- Subject: Authorization to IT4Innovations
-
- Dear support,
-
- Please include my collaborators to project OPEN-0-0.
-
- John Smith, john.smith@myemail.com, Department of Chemistry, MIT, US
- Jonas Johansson, jjohansson@otheremail.se, Department of Physics, Royal Institute of Technology, Sweden
- Luisa Fibonacci, lf@emailitalia.it, Department of Mathematics, National Research Council, Italy
-
- Thank you,
- PI
- (Digitally signed)
-
-Should the above information be provided by e-mail, the e-mail **must
-be** digitally signed. Read more on [digital
-signatures](obtaining-login-credentials.html#the-certificates-for-digital-signatures)
-below.
-
-The Login Credentials
--------------------------
-
-Once authorized by PI, every person (PI or Collaborator) wishing to
-access the clusters, should contact the [IT4I
-support](https://support.it4i.cz/rt/) (E-mail: [support
-[at] it4i.cz](mailto:support%20%5Bat%5D%20it4i.cz)) providing
-following information:
-
-1. Project ID
-2. Full name and affiliation
-3. Statement that you have read and accepted the [Acceptable use policy
- document](http://www.it4i.cz/acceptable-use-policy.pdf) (AUP).
-4. Attach the AUP file.
-5. Your preferred username, max 8 characters long. The preferred
- username must associate your surname and name or be otherwise
- derived from it. Only alphanumeric sequences, dash and underscore
- signs are allowed.
-6. In case you choose [Alternative way to personal
- certificate](obtaining-login-credentials.html#alternative-way-of-getting-personal-certificate),
- a **scan of photo ID** (personal ID or passport or driver license)
- is required
-
-Example (except the subject line which must be in English, you may use
-Czech or Slovak language for communication with us):
-
- Subject: Access to IT4Innovations
-
- Dear support,
-
- Please open the user account for me and attach the account to OPEN-0-0
- Name and affiliation: John Smith, john.smith@myemail.com, Department of Chemistry, MIT, US
- I have read and accept the Acceptable use policy document (attached)
-
- Preferred username: johnsm
-
- Thank you,
- John Smith
- (Digitally signed)
-
-Should the above information be provided by e-mail, the e-mail **must
-be** digitally signed. To sign an e-mail, you need digital certificate.
-Read more on [digital
-signatures](obtaining-login-credentials.html#the-certificates-for-digital-signatures)
-below.
-
-Digital signature allows us to confirm your identity in remote
-electronic communication and provides an encrypted channel to exchange
-sensitive information such as login credentials. After receiving your
-signed e-mail with the requested information, we will send you your
-login credentials (user name, key, passphrase and password) to access
-the IT4I systems.
-
-We accept certificates issued by any widely respected certification
-authority.
-
-For various reasons we do not accept PGP keys.** Please, use only
-X.509 PKI certificates for communication with us.**
-
-You will receive your personal login credentials by protected e-mail.
-The login credentials include:
-
-1. username
-2. ssh private key and private key passphrase
-3. system password
-
-The clusters are accessed by the [private
-key](../accessing-the-clusters/shell-access-and-data-transfer/ssh-keys.html)
-and username.
-Username and password is used for login to the information systems
-listed on <http://support.it4i.cz/>.
-
-### Change Passphrase
-
-On Linux, use
-
-`
-local $ ssh-keygen -f id_rsa -p
-`
-
-On Windows, use [PuTTY Key
-Generator](../accessing-the-clusters/shell-access-and-data-transfer/putty/puttygen.html).
-
-### Change Password
-
-Change password in your user profile atÂ
-<https://extranet.it4i.cz/user/>
-
-The Certificates for Digital Signatures
--------------------------------------------
-
-We accept personal certificates issued by any widely respected
-certification authority (CA). This includes certificates by CAs
-organized in International Grid Trust Federation
-(<http://www.igtf.net/>), its European branch EUGridPMA -
-<https://www.eugridpma.org/> and its member organizations, e.g. the
-CESNET certification authority - <https://tcs-p.cesnet.cz/confusa/>. The
-Czech *"Qualified certificate" (Kvalifikovaný certifikát)* (provided by
-<http://www.postsignum.cz/> or
-<http://www.ica.cz/Kvalifikovany-certifikat.aspx>), that is used in
-electronic contact with Czech authorities is accepted as well.
-
-Certificate generation process is well-described here:
-
-- [How to generate a personal TCS certificate in Mozilla Firefox web
- browser
- (in Czech)](http://idoc.vsb.cz/xwiki/wiki/infra/view/uzivatel/moz-cert-gen)
-
-Â
-
-A FAQ about certificates can be found here: >[Certificates
-FAQ](certificates-faq.html).
-
-Alternative Way to Personal Certificate
--------------------------------------------
-
-Follow these steps **only** if you can not obtain your certificate in a
-standard way.
-In case you choose this procedure, please attach a **scan of photo ID**
-(personal ID or passport or drivers license) when applying for [login
-credentials](obtaining-login-credentials.html#the-login-credentials).
-
-1. Go to <https://www.cacert.org/>.
- - If there's a security warning, just acknowledge it.
-
-2. Click *Join*.
-3. Fill in the form and submit it by the *Next* button.
- - Type in the e-mail address which you use for communication
- with us.
- - Don't forget your chosen *Pass Phrase*.
-
-4. You will receive an e-mail verification link. Follow it.
-5. After verifying, go to the CAcert's homepage and login using
- *Password Login*.
-6. Go to *Client Certificates* -> *New*.
-7. Tick *Add* for your e-mail address and click the *Next* button.
-8. Click the *Create Certificate Request* button.
-9. You'll be redirected to a page from where you can download/install
- your certificate.
- - Simultaneously you'll get an e-mail with a link to
- the certificate.
-
-Installation of the Certificate Into Your Mail Client
------------------------------------------------------
-
-The procedure is similar to the following guides:
-
-- MS Outlook 2010
- - [How to Remove, Import, and Export Digital
- Certificates](http://support.microsoft.com/kb/179380)
- - [Importing a PKCS #12 certificate
- (in Czech)](http://idoc.vsb.cz/xwiki/wiki/infra/view/uzivatel/outl-cert-imp)
-- Mozilla Thudnerbird
- - [Installing an SMIME
- certificate](http://kb.mozillazine.org/Installing_an_SMIME_certificate)
- - [Importing a PKCS #12 certificate
- (in Czech)](http://idoc.vsb.cz/xwiki/wiki/infra/view/uzivatel/moz-cert-imp)
-
-End of User Account Lifecycle
------------------------------
-
-User accounts are supported by membership in active Project(s) or by
-affiliation to IT4Innovations. User accounts, that loose the support
-(meaning, are not attached to an active project and are not affiliated
-with IT4I), will be deleted 1 year after the last project to which they
-were attached expires.
-
-User will get 3 automatically generated warning e-mail messages of the
-pending removal:.
-
-- First message will be sent 3 months before the removal
-- Second message will be sent 1 month before the removal
-- Third message will be sent 1 week before the removal.
-
-The messages will inform about the projected removal date and will
-challenge the user to migrate her/his data
-
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-Documentation
-=============
-
-
-
-Welcome to IT4Innovations documentation pages. The IT4Innovations
-national supercomputing center operates supercomputers
-[Salomon](salomon.html) and
-[Anselm](anselm.html). The supercomputers are [
-available](get-started-with-it4innovations/applying-for-resources.html)
-to academic community within the Czech Republic and Europe and
-industrial community worldwide. The purpose of these pages is to provide
-a comprehensive documentation on hardware, software and usage of the
-computers.
-
- How to read the documentation
---------------------------------------------------------------------------------------------------
-
-1. Read the list in the left column. Select the subject of interest.
- Alternatively, use the Search box in the upper right corner.
-2. Read the CONTENTS in the upper right corner.
-3. Scan for all the yellow bulb call-outs on the page.
-4. Read the details if still more information is needed. **Look for
- examples** illustrating the concepts.
-
-Â
-
-The call-out. Â Focus on the call-outs before reading full details.
-
-Â
-
-- Read the
- [Changelog](get-started-with-it4innovations/changelog.html)
- to keep up to date.
-
-Getting Help and Support
-------------------------
-
-Contact [support [at]
-it4i.cz](mailto:support%20%5Bat%5D%20it4i.cz) for help and
-support regarding the cluster technology at IT4Innovations.
-Please use **Czech**, **Slovak** or **English** language for
-communication with us.
-Follow the status of your request to IT4Innovations at
-[support.it4i.cz/rt](http://support.it4i.cz/rt).
-
-Â
-
-Use your IT4Innotations username and password to log in to the
-[support](http://support.it4i.cz/) portal.
-
-Required Proficiency
---------------------
-
-You need basic proficiency in Linux environment.
-
-Â
-
-In order to use the system for your calculations, you need basic
-proficiency in Linux environment. To gain the proficiency, we recommend
-you reading the [ introduction to
-Linux](http://www.tldp.org/LDP/intro-linux/html/)
-operating system environment and installing a Linux distribution on your
-personal computer. A good choice might be the [
-Fedora](http://fedoraproject.org/)
-distribution, as it is similar to systems on the clusters at
-IT4Innovations. It's easy to install and use. In fact, any distribution
-would do.
-
-Â
-
-Learn how to parallelize your code!
-
-Â
-
-In many cases, you will run your own code on the cluster. In order to
-fully exploit the cluster, you will need to carefully consider how to
-utilize all the cores available on the node and how to use multiple
-nodes at the same time. You need to **parallelize** your code.
-Proficieny in MPI, OpenMP, CUDA, UPC or GPI2 programming may be gained
-via the [training provided by
-IT4Innovations.](http://prace.it4i.cz)
-
-Terminology Frequently Used on These Pages
-------------------------------------------
-
-- **node:** a computer, interconnected by network to other computers -
- Computational nodes are powerful computers, designed and dedicated
- for executing demanding scientific computations.
-- **core:** processor core, a unit of processor, executing
- computations
-- **corehours:** wall clock hours of processor core time - Each node
- is equipped with **X** processor cores, provides **X** corehours per
- 1 wall clock hour.
-- **job:** a calculation running on the supercomputer - The job
- allocates and utilizes resources of the supercomputer for
- certain time.
-- **HPC:** High Performance Computing
-- **HPC (computational) resources:** corehours, storage capacity,
- software licences
-- **code:** a program
-- **primary investigator (PI):** a person responsible for execution of
- computational project and utilization of computational resources
- allocated to that project
-- **collaborator:** a person participating on execution of
- computational project and utilization of computational resources
- allocated to that project
-- **project:** a computational project under investigation by the
- PI - The project is identified by the project ID. The computational
- resources are allocated and charged per project.
-- **jobscript:** a script to be executed by the PBS Professional
- workload manager
-
-Conventions
------------
-
-In this documentation, you will find a number of pages containing
-examples. We use the following conventions:
-
-Â Cluster command prompt
-
-`
-$
-`
-
-Your local linux host command prompt
-
-`
-local $
-`
-
-Â Errata
--------
-
-Although we have taken every care to ensure the accuracy of our content,
-mistakes do happen. If you find a mistake in the text or the code we
-would be grateful if you would report this to us. By doing so, you can
-save other readers from frustration and help us improve subsequent
-versions of this documentation. If you find any errata, please report
-them by visiting http://support.it4i.cz/rt, creating a new ticket, and
-entering the details of your errata. Once your errata are verified, your
-submission will be accepted and the errata will be uploaded on our
-website.
-
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-Shell access and data transfer
-==============================
-
-
-
-Interactive Login
------------------
-
-The Salomon cluster is accessed by SSH protocol via login nodes login1,
-login2, login3 and login4 at address salomon.it4i.cz. The login nodes
-may be addressed specifically, by prepending the login node name to the
-address.
-
-The alias >salomon.it4i.cz is currently not available through VPN
-connection. Please use loginX.salomon.it4i.cz when connected to
-VPN.
-
- |Login address|Port|Protocol|Login node|
- |---|---|---|---|
- |salomon.it4i.cz|22|ssh|round-robin DNS record for login[1-4]|
- |login1.salomon.it4i.cz|22|ssh|login1|
- |login1.salomon.it4i.cz|22|ssh|login1|
- |login1.salomon.it4i.cz|22|ssh|login1|
- |login1.salomon.it4i.cz|22|ssh|login1|
-
-The authentication is by the [private
-key](../get-started-with-it4innovations/accessing-the-clusters/shell-access-and-data-transfer/ssh-keys.html)
-
-Please verify SSH fingerprints during the first logon. They are
-identical on all login nodes:
-f6:28:98:e4:f9:b2:a6:8f:f2:f4:2d:0a:09:67:69:80Â (DSA)
-70:01:c9:9a:5d:88:91:c7:1b:c0:84:d1:fa:4e:83:5c (RSA)
-
-Â
-
-Private key (`id_rsa/id_rsa.ppk` ): `600 (-rw-------)`s authentication:
-
-On **Linux** or **Mac**, use
-
-`
-local $ ssh -i /path/to/id_rsa username@salomon.it4i.cz
-`
-
-If you see warning message "UNPROTECTED PRIVATE KEY FILE!", use this
-command to set lower permissions to private key file.
-
-`
-local $ chmod 600 /path/to/id_rsa
-`
-
-On **Windows**, use [PuTTY ssh
-client](../get-started-with-it4innovations/accessing-the-clusters/shell-access-and-data-transfer/putty/putty.html).
-
-After logging in, you will see the command prompt:
-
- Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â _____Â Â Â Â Â Â _Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â
- Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â / ____|Â Â Â Â | |Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â
- Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â | (___Â Â __ _| | ___Â _ __ ___Â Â ___Â _ __ Â
- Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â ___ / _` | |/ _ | '_ ` _ / _ | '_
- Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â ____) | (_| | | (_) | | | | | | (_) | | | |
- Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â |_____/ __,_|_|___/|_| |_| |_|___/|_| |_|
- Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â
-
- Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â http://www.it4i.cz/?lang=en
-
- Last login: Tue Jul 9 15:57:38 2013 from your-host.example.com
- [username@login2.salomon ~]$
-
-The environment is **not** shared between login nodes, except for
-[shared filesystems](storage/storage.html).
-
-Data Transfer
--------------
-
-Data in and out of the system may be transferred by the
-[scp](http://en.wikipedia.org/wiki/Secure_copy) and sftp
-protocols.
-
-In case large volumes of data are transferred, use dedicated data mover
-nodes cedge[1-3].salomon.it4i.cz for increased performance.
-
-Â
-
-HTML commented section #1 (removed cedge servers from the table)
-
- Address |Port|Protocol|
- ----------------------- |---|---|------------
- salomon.it4i.cz 22 scp, sftp
- login1.salomon.it4i.cz 22 scp, sftp
- login2.salomon.it4i.cz 22 scp, sftp
- login3.salomon.it4i.cz 22 scp, sftp
- login4.salomon.it4i.cz 22 scp, sftp
-
-Â The authentication is by the [private
-key](../get-started-with-it4innovations/accessing-the-clusters/shell-access-and-data-transfer/ssh-keys.html)
-
-HTML commented section #2 (ssh transfer performance data need to be
-verified)
-
-On linux or Mac, use scp or sftp client to transfer the data to Salomon:
-
-`
-local $ scp -i /path/to/id_rsa my-local-file username@salomon.it4i.cz:directory/file
-`
-
-`
-local $ scp -i /path/to/id_rsa -r my-local-dir username@salomon.it4i.cz:directory
-`
-
- or
-
-`
-local $ sftp -o IdentityFile=/path/to/id_rsa username@salomon.it4i.cz
-`
-
-Very convenient way to transfer files in and out of the Salomon computer
-is via the fuse filesystem
-[sshfs](http://linux.die.net/man/1/sshfs)
-
-`
-local $ sshfs -o IdentityFile=/path/to/id_rsa username@salomon.it4i.cz:. mountpoint
-`
-
-Using sshfs, the users Salomon home directory will be mounted on your
-local computer, just like an external disk.
-
-Learn more on ssh, scp and sshfs by reading the manpages
-
-`
-$ man ssh
-$ man scp
-$ man sshfs
-`
-
-On Windows, use [WinSCP
-client](http://winscp.net/eng/download.php) to transfer
-the data. The [win-sshfs
-client](http://code.google.com/p/win-sshfs/) provides a
-way to mount the Salomon filesystems directly as an external disc.
-
-More information about the shared file systems is available
-[here](storage/storage.html).
-
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-Outgoing connections
-====================
-
-
-
-Connection restrictions
------------------------
-
-Outgoing connections, from Salomon Cluster login nodes to the outside
-world, are restricted to following ports:
-
- |Port|Protocol|
- |---|---|
- |22|ssh|
- |80|http|
- |443|https|
- |9418|git|
-
-Please use **ssh port forwarding** and proxy servers to connect from
-Salomon to all other remote ports.
-
-Outgoing connections, from Salomon Cluster compute nodes are restricted
-to the internal network. Direct connections form compute nodes to
-outside world are cut.
-
-Port forwarding
----------------
-
-### Port forwarding from login nodes
-
-Port forwarding allows an application running on Salomon to connect to
-arbitrary remote host and port.
-
-It works by tunneling the connection from Salomon back to users
-workstation and forwarding from the workstation to the remote host.
-
-Pick some unused port on Salomon login node (for example 6000) and
-establish the port forwarding:
-
-`
-local $ ssh -R 6000:remote.host.com:1234 salomon.it4i.cz
-`
-
-In this example, we establish port forwarding between port 6000 on
-Salomon and port 1234 on the remote.host.com. By accessing
-localhost:6000 on Salomon, an application will see response of
-remote.host.com:1234. The traffic will run via users local workstation.
-
-Port forwarding may be done **using PuTTY** as well. On the PuTTY
-Configuration screen, load your Salomon configuration first. Then go to
-Connection->SSH->Tunnels to set up the port forwarding. Click
-Remote radio button. Insert 6000 to Source port textbox. Insert
-remote.host.com:1234. Click Add button, then Open.
-
-Port forwarding may be established directly to the remote host. However,
-this requires that user has ssh access to remote.host.com
-
-`
-$ ssh -L 6000:localhost:1234 remote.host.com
-`
-
-Note: Port number 6000 is chosen as an example only. Pick any free port.
-
-### Port forwarding from compute nodes
-
-Remote port forwarding from compute nodes allows applications running on
-the compute nodes to access hosts outside Salomon Cluster.
-
-First, establish the remote port forwarding form the login node, as
-[described
-above](outgoing-connections.html#port-forwarding-from-login-nodes).
-
-Second, invoke port forwarding from the compute node to the login node.
-Insert following line into your jobscript or interactive shell
-
-`
-$ ssh -TN -f -L 6000:localhost:6000 login1
-`
-
-In this example, we assume that port forwarding from login1:6000 to
-remote.host.com:1234 has been established beforehand. By accessing
-localhost:6000, an application running on a compute node will see
-response of remote.host.com:1234
-
-### Using proxy servers
-
-Port forwarding is static, each single port is mapped to a particular
-port on remote host. Connection to other remote host, requires new
-forward.
-
-Applications with inbuilt proxy support, experience unlimited access to
-remote hosts, via single proxy server.
-
-To establish local proxy server on your workstation, install and run
-SOCKS proxy server software. On Linux, sshd demon provides the
-functionality. To establish SOCKS proxy server listening on port 1080
-run:
-
-`
-local $ ssh -D 1080 localhost
-`
-
-On Windows, install and run the free, open source [Sock
-Puppet](http://sockspuppet.com/) server.
-
-Once the proxy server is running, establish ssh port forwarding from
-Salomon to the proxy server, port 1080, exactly as [described
-above](outgoing-connections.html#port-forwarding-from-login-nodes).
-
-`
-local $ ssh -R 6000:localhost:1080 salomon.it4i.cz
-`
-
-Now, configure the applications proxy settings to **localhost:6000**.
-Use port forwarding to access the [proxy server from compute
-nodes](outgoing-connections.html#port-forwarding-from-compute-nodes)
-as well .
-
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-VPN Access
-==========
-
-
-
-Accessing IT4Innovations internal resources via VPN
----------------------------------------------------
-
-For using resources and licenses which are located at IT4Innovations
-local network, it is necessary to VPN connect to this network.
-We use Cisco AnyConnect Secure Mobility Client, which is supported on
-the following operating systems:
-
-- >Windows XP
-- >Windows Vista
-- >Windows 7
-- >Windows 8
-- >Linux
-- >MacOS
-
-It is impossible to connect to VPN from other operating systems.
-
-VPN client installation
-------------------------------------
-
-You can install VPN client from web interface after successful login
-with LDAP credentials on address <https://vpn.it4i.cz/user>
-
-
-
-According to the Java settings after login, the client either
-automatically installs, or downloads installation file for your
-operating system. It is necessary to allow start of installation tool
-for automatic installation.
-
-
-
-
-
-
-After successful installation, VPN connection will be established and
-you can use available resources from IT4I network.
-
-
-
-If your Java setting doesn't allow automatic installation, you can
-download installation file and install VPN client manually.
-
-
-
-After you click on the link, download of installation file will start.
-
-
-
-After successful download of installation file, you have to execute this
-tool with administrator's rights and install VPN client manually.
-
-Working with VPN client
------------------------
-
-You can use graphical user interface or command line interface to run
-VPN client on all supported operating systems. We suggest using GUI.
-
-Before the first login to VPN, you have to fill
-URLÂ **https://vpn.it4i.cz/user** into the text field.
-
- Contacting
-
-
-After you click on the Connect button, you must fill your login
-credentials.
-
- Contacting
-
-
-After a successful login, the client will minimize to the system tray.
-If everything works, you can see a lock in the Cisco tray icon.
-
-[
-
-
-If you right-click on this icon, you will see a context menu in which
-you can control the VPN connection.
-
-[
-
-
-When you connect to the VPN for the first time, the client downloads the
-profile and creates a new item "IT4I cluster" in the connection list.
-For subsequent connections, it is not necessary to re-enter the URL
-address, but just select the corresponding item.
-
- Contacting
-
-
-Then AnyConnect automatically proceeds like in the case of first logon.
-
-
-
-After a successful logon, you can see a green circle with a tick mark on
-the lock icon.
-
- Succesfull
-
-
-For disconnecting, right-click on the AnyConnect client icon in the
-system tray and select **VPN Disconnect**.
-
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-Environment and Modules
-=======================
-
-
-
-### Environment Customization
-
-After logging in, you may want to configure the environment. Write your
-preferred path definitions, aliases, functions and module loads in the
-.bashrc file
-
-`
-# ./bashrc
-
-# Source global definitions
-if [ -f /etc/bashrc ]; then
- . /etc/bashrc
-fi
-
-# User specific aliases and functions
-alias qs='qstat -a'
-module load intel/2015b
-
-# Display informations to standard output - only in interactive ssh session
-if [ -n "$SSH_TTY" ]
-then
- module list # Display loaded modules
-fi
-`
-
-Do not run commands outputing to standard output (echo, module list,
-etc) in .bashrc for non-interactive SSH sessions. It breaks fundamental
-functionality (scp, PBS) of your account! Take care for SSH session
-interactivity for such commands as
- stated in the previous example.
-in the previous example.
-
-### Application Modules
-
-In order to configure your shell for running particular application on
-Salomon we use Module package interface.
-
-Application modules on Salomon cluster are built using
-[EasyBuild](http://hpcugent.github.io/easybuild/ "EasyBuild"). The
-modules are divided into the following structure:
-
-`
- base: Default module class
- bio: Bioinformatics, biology and biomedical
- cae: Computer Aided Engineering (incl. CFD)
- chem: Chemistry, Computational Chemistry and Quantum Chemistry
- compiler: Compilers
- data: Data management & processing tools
- debugger: Debuggers
- devel: Development tools
- geo: Earth Sciences
- ide: Integrated Development Environments (e.g. editors)
- lang: Languages and programming aids
- lib: General purpose libraries
- math: High-level mathematical software
- mpi: MPI stacks
- numlib: Numerical Libraries
- perf: Performance tools
- phys: Physics and physical systems simulations
- system: System utilities (e.g. highly depending on system OS and hardware)
- toolchain: EasyBuild toolchains
- tools: General purpose tools
- vis: Visualization, plotting, documentation and typesetting
-`
-
-The modules set up the application paths, library paths and environment
-variables for running particular application.
-
-The modules may be loaded, unloaded and switched, according to momentary
-needs.
-
-To check available modules use
-
-`
-$ module avail
-`
-
-To load a module, for example the OpenMPI module use
-
-`
-$ module load OpenMPI
-`
-
-loading the OpenMPI module will set up paths and environment variables
-of your active shell such that you are ready to run the OpenMPI software
-
-To check loaded modules use
-
-`
-$ module list
-`
-
-Â To unload a module, for example the OpenMPI module use
-
-`
-$ module unload OpenMPI
-`
-
-Learn more on modules by reading the module man page
-
-`
-$ man module
-`
-
-### EasyBuild Toolchains
-
-As we wrote earlier, we are using EasyBuild for automatised software
-installation and module creation.Â
-
-EasyBuild employs so-called **compiler toolchains** or,
-simply toolchains for short, which are a major concept in handling the
-build and installation processes.
-
-A typical toolchain consists of one or more compilers, usually put
-together with some libraries for specific functionality, e.g., for using
-an MPI stack for distributed computing, or which provide optimized
-routines for commonly used math operations, e.g., the well-known
-BLAS/LAPACK APIs for linear algebra routines.
-
-For each software package being built, the toolchain to be used must be
-specified in some way.
-
-The EasyBuild framework prepares the build environment for the different
-toolchain components, by loading their respective modules and defining
-environment variables to specify compiler commands (e.g.,
-via `$F90`), compiler and linker options (e.g.,
-via `$CFLAGS` and `$LDFLAGS`{.docutils .literal}),
-the list of library names to supply to the linker (via `$LIBS`{.docutils
-.literal}), etc. This enables making easyblocks
-largely toolchain-agnostic since they can simply rely on these
-environment variables; that is, unless they need to be aware of, for
-example, the particular compiler being used to determine the build
-configuration options.
-
-Recent releases of EasyBuild include out-of-the-box toolchain support
-for:
-
-- various compilers, including GCC, Intel, Clang, CUDA
-- common MPI libraries, such as Intel MPI, MPICH, MVAPICH2, OpenMPI
-- various numerical libraries, including ATLAS, Intel MKL, OpenBLAS,
- ScalaPACK, FFTW
-
-Â
-
-On Salomon, we have currently following toolchains installed:
-
- |Toolchain|Module(s)|
- |---|----|
- |GCC|GCC|
- |ictce|icc, ifort, imkl, impi|
- |intel|GCC, icc, ifort, imkl, impi|
- |gompi|GCC, OpenMPI|
- |goolf|BLACS, FFTW, GCC, OpenBLAS, OpenMPI, ScaLAPACK|
- |iompi|OpenMPI, icc, ifort|
- |iccifort|icc, ifort|
-
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-Hardware Overview
-=================
-
-
-
-Introduction
-------------
-
-The Salomon cluster consists of 1008 computational nodes of which 576
-are regular compute nodes and 432 accelerated nodes. Each node is a
- powerful x86-64 computer, equipped
-with 24 cores (two twelve-core Intel Xeon processors) and 128GB RAM. The
-nodes are interlinked by high speed InfiniBand and Ethernet networks.
-All nodes share 0.5PB /home NFS disk storage to store the user files.
-Users may use a DDN Lustre shared storage with capacity of 1.69 PB which
-is available for the scratch project data. The user access to the
-Salomon cluster is provided by four login nodes.
-
-[More about schematic representation of the Salomon cluster compute
-nodes IB
-topology](../network-1/ib-single-plane-topology.html).
-
-
-
-The parameters are summarized in the following tables:
-
-General information
--------------------
-
-In general**
-Primary purpose
-High Performance Computing
-Architecture of compute nodes
-x86-64
-Operating system
-CentOS 6.7 Linux
-[**Compute nodes**](../compute-nodes.html)
-Totally
-1008
-Processor
-2x Intel Xeon E5-2680v3, 2.5GHz, 12cores
-RAM
-128GB, 5.3GB per core, DDR4@2133 MHz
-Local disk drive
-no
-Compute network / Topology
-InfiniBand FDR56 / 7D Enhanced hypercube
-w/o accelerator
-576
-MIC accelerated
-432
-In total**
-Total theoretical peak performance (Rpeak)
-2011 Tflop/s
-Total amount of RAM
-129.024 TB
-Compute nodes
--------------
-
- |Node|Count|Processor|Cores|Memory|Accelerator|
- ----------------- - |---|---|------------------------ ------- -------- --------------------------------------------
- |w/o accelerator|576|2x Intel Xeon E5-2680v3, 2.5GHz|24|128GB|-|
- |MIC accelerated|432|2x Intel Xeon E5-2680v3, 2.5GHz|24|128GB|2x Intel Xeon Phi 7120P, 61cores, 16GB RAM|
-
-For more details please refer to the [Compute
-nodes](../compute-nodes.html).
-
-Remote visualization nodes
---------------------------
-
-For remote visualization two nodes with NICE DCV software are available
-each configured:
-
- |Node|Count|Processor|Cores|Memory|GPU Accelerator|
- --------------- - |---|---|----------------------- ------- -------- ------------------------------
- |visualization|2|2x Intel Xeon E5-2695v3, 2.3GHz|28|512GB|NVIDIA QUADRO K5000, 4GB RAM|
-
-SGI UV 2000
------------
-
-For large memory computations a special SMP/NUMA SGI UV 2000 server is
-available:
-
- |Node |Count |Processor |Cores<th align="left">Memory<th align="left">Extra HW |
- | --- | --- |
- |UV2000 |1 |14x Intel Xeon E5-4627v2, 3.3GHz, 8cores |112 |3328GB DDR3@1866MHz |2x 400GB local SSD1x NVIDIA GM200(GeForce GTX TITAN X),12GB RAM\ |
-
-
-
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-Introduction
-============
-
-Welcome to Salomon supercomputer cluster. The Salomon cluster consists
-of 1008 compute nodes, totaling 24192 compute cores with 129TB RAM and
-giving over 2 Pflop/s theoretical peak performance. Each node is a
-powerful x86-64 computer, equipped with 24
-cores, at least 128GB RAM. Nodes are interconnected by 7D Enhanced
-hypercube Infiniband network and equipped with Intel Xeon E5-2680v3
-processors. The Salomon cluster consists of 576 nodes without
-accelerators and 432 nodes equipped with Intel Xeon Phi MIC
-accelerators. Read more in [Hardware
-Overview](hardware-overview-1/hardware-overview.html).
-
-The cluster runs CentOS Linux [
-](http://www.bull.com/bullx-logiciels/systeme-exploitation.html)
-operating system, which is compatible with
-the RedHat [
-Linux
-family.](http://upload.wikimedia.org/wikipedia/commons/1/1b/Linux_Distribution_Timeline.svg)
-
-**Water-cooled Compute Nodes With MIC Accelerator**
-
-
-
-
-
-**Tape Library T950B**
-
-
-
-
-
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-7D Enhanced Hypercube
-=====================
-
-[More about Job submission - Placement by IB switch / Hypercube
-dimension.](../resource-allocation-and-job-execution/job-submission-and-execution.html)
-
-Nodes may be selected via the PBS resource attribute ehc_[1-7]d .
-
- |Hypercube|dimension|
- --------------- |---|---|---------------------------------
- |1D|ehc_1d|
- |2D|ehc_2d|
- |3D|ehc_3d|
- |4D|ehc_4d|
- |5D|ehc_5d|
- |6D|ehc_6d|
- |7D|ehc_7d|
-
-[Schematic representation of the Salomon cluster IB single-plain
-topology represents hypercube
-dimension 0](ib-single-plane-topology.html).
-
-### 7D Enhanced Hypercube {#d-enhanced-hypercube}
-
-
-
-Â
-
- |Node type|Count|Short name|Long name|Rack|
- -------------------------------------- - |---|---|-------- -------------------------- -------
- |M-Cell compute nodes w/o accelerator|576|cns1 -cns576|r1i0n0 - r4i7n17|1-4|
- |compute nodes MIC accelerated|432|cns577 - cns1008|r21u01n577 - r37u31n1008|21-38|
-
-### Â IB Topology
-
-
-
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-IB single-plane topology
-========================
-
-
-
-A complete M-Cell assembly consists of four compute racks. Each rack
-contains 4x physical IRUs - Independent rack units. Using one dual
-socket node per one blade slot leads to 8 logical IRUs. Each rack
-contains 4x2 SGI ICE X IB Premium Blades.
-
-The SGI ICE X IB Premium Blade provides the first level of
-interconnection via dual 36-port Mellanox FDR InfiniBand ASIC switch
-with connections as follows:
-
-- 9 ports from each switch chip connect to the unified backplane, to
- connect the 18 compute node slots
-- 3 ports on each chip provide connectivity between the chips
-- 24 ports from each switch chip connect to the external bulkhead, for
- a total of 48
-
-###IB single-plane topology - ICEX Mcell
-
-Each colour in each physical IRU represents one dual-switch ASIC switch.
-
-
-
-Â
-
-### IB single-plane topology - Accelerated nodes
-
-Each of the 3 inter-connected D racks are equivalent to one half of
-Mcell rack. 18x D rack with MIC accelerated nodes [r21-r38] are
-equivalent to 3 Mcell racks as shown in a diagram [7D Enhanced
-Hypercube](7d-enhanced-hypercube.html).
-
-As shown in a diagram :
-
-- Racks 21, 22, 23, 24, 25, 26 are equivalent to one Mcell rack.
-- Racks 27, 28, 29, 30, 31, 32 are equivalent to one Mcell rack.
-- Racks 33, 34, 35, 36, 37, 38 are equivalent to one Mcell rack.
-
-
-
-Â
-
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-Network
-=======
-
-
-
-All compute and login nodes of Salomon are interconnected by 7D Enhanced
-hypercube
-[Infiniband](http://en.wikipedia.org/wiki/InfiniBand)
-network and by Gigabit
-[Ethernet](http://en.wikipedia.org/wiki/Ethernet)
-network. Only
-[Infiniband](http://en.wikipedia.org/wiki/InfiniBand)
-network may be used to transfer user data.
-
-Infiniband Network
-------------------
-
-All compute and login nodes of Salomon are interconnected by 7D Enhanced
-hypercube
-[Infiniband](http://en.wikipedia.org/wiki/InfiniBand)
-network (56 Gbps). The network topology is a [7D Enhanced
-hypercube](7d-enhanced-hypercube.html).
-
-Read more about schematic representation of the Salomon cluster [IB
-single-plain topology](ib-single-plane-topology.html)
-([hypercube dimension](7d-enhanced-hypercube.html)
-0).[>](IB%20single-plane%20topology%20-%20Accelerated%20nodes.pdf/view.html)
-
-The compute nodes may be accessed via the Infiniband network using ib0
-network interface, in address range 10.17.0.0 (mask 255.255.224.0). The
-MPI may be used to establish native Infiniband connection among the
-nodes.
-
-The network provides **2170MB/s** transfer rates via the TCP connection
-(single stream) and up to **3600MB/s** via native Infiniband protocol.
-
-Â
-
-Example
--------
-
-`
-$ qsub -q qexp -l select=4:ncpus=16 -N Name0 ./myjob
-$ qstat -n -u username
- Req'd Req'd Elap
-Job ID Username Queue Jobname SessID NDS TSK Memory Time S Time
---------------- -------- -- |---|---| ------ --- --- ------ ----- - -----
-15209.isrv5 username qexp Name0 5530 4 96 -- 01:00 R 00:00
- r4i1n0/0*24+r4i1n1/0*24+r4i1n2/0*24+r4i1n3/0*24
-`
-
-In this example, we access the node r4i1n0 by Infiniband network via the
-ib0 interface.
-
-`
-$ ssh 10.17.35.19
-`
-
-In this example, we get
-information of the Infiniband network.
-
-`
-$ ifconfig
-....
-inet addr:10.17.35.19....
-....
-
-$ ip addr show ib0
-
-....
-inet 10.17.35.19....
-....
-`
-
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-PRACE User Support
-==================
-
-
-
-Intro
------
-
-PRACE users coming to Salomon as to TIER-1 system offered through the
-DECI calls are in general treated as standard users and so most of the
-general documentation applies to them as well. This section shows the
-main differences for quicker orientation, but often uses references to
-the original documentation. PRACE users who don't undergo the full
-procedure (including signing the IT4I AuP on top of the PRACE AuP) will
-not have a password and thus access to some services intended for
-regular users. This can lower their comfort, but otherwise they should
-be able to use the TIER-1 system as intended. Please see the [Obtaining
-Login Credentials
-section](../get-started-with-it4innovations/obtaining-login-credentials/obtaining-login-credentials.html),
-if the same level of access is required.
-
-All general [PRACE User
-Documentation](http://www.prace-ri.eu/user-documentation/)
-should be read before continuing reading the local documentation here.
-
-[]()Help and Support
-------------------------
-
-If you have any troubles, need information, request support or want to
-install additional software, please use [PRACE
-Helpdesk](http://www.prace-ri.eu/helpdesk-guide264/).
-
-Information about the local services are provided in the [introduction
-of general user documentation](introduction.html).
-Please keep in mind, that standard PRACE accounts don't have a password
-to access the web interface of the local (IT4Innovations) request
-tracker and thus a new ticket should be created by sending an e-mail to
-support[at]it4i.cz.
-
-Obtaining Login Credentials
----------------------------
-
-In general PRACE users already have a PRACE account setup through their
-HOMESITE (institution from their country) as a result of rewarded PRACE
-project proposal. This includes signed PRACE AuP, generated and
-registered certificates, etc.
-
-If there's a special need a PRACE user can get a standard (local)
-account at IT4Innovations. To get an account on the Salomon cluster, the
-user needs to obtain the login credentials. The procedure is the same as
-for general users of the cluster, so please see the corresponding
-[section of the general documentation
-here](../get-started-with-it4innovations/obtaining-login-credentials.html).
-
-Accessing the cluster
----------------------
-
-### Access with GSI-SSH
-
-For all PRACE users the method for interactive access (login) and data
-transfer based on grid services from Globus Toolkit (GSI SSH and
-GridFTP) is supported.
-
-The user will need a valid certificate and to be present in the PRACE
-LDAP (please contact your HOME SITE or the primary investigator of your
-project for LDAP account creation).
-
-Most of the information needed by PRACE users accessing the Salomon
-TIER-1 system can be found here:
-
-- [General user's
- FAQ](http://www.prace-ri.eu/Users-General-FAQs)
-- [Certificates
- FAQ](http://www.prace-ri.eu/Certificates-FAQ)
-- [Interactive access using
- GSISSH](http://www.prace-ri.eu/Interactive-Access-Using-gsissh)
-- [Data transfer with
- GridFTP](http://www.prace-ri.eu/Data-Transfer-with-GridFTP-Details)
-- [Data transfer with
- gtransfer](http://www.prace-ri.eu/Data-Transfer-with-gtransfer)
-
-Â
-
-Before you start to use any of the services don't forget to create a
-proxy certificate from your certificate:
-
- $ grid-proxy-init
-
-To check whether your proxy certificate is still valid (by default it's
-valid 12 hours), use:
-
- $ grid-proxy-info
-
-Â
-
-To access Salomon cluster, two login nodes running GSI SSH service are
-available. The service is available from public Internet as well as from
-the internal PRACE network (accessible only from other PRACE partners).
-
-***Access from PRACE network:**
-
-It is recommended to use the single DNS name
-salomon-prace.it4i.cz which is distributed
-between the two login nodes. If needed, user can login directly to one
-of the login nodes. The addresses are:
-
- |Login address|Port|Protocol|Login node|
- |---|---|
- |salomon-prace.it4i.cz|2222|gsissh|login1, login2, login3 or login4|
- |login1-prace.salomon.it4i.cz|2222|gsissh|login1|
- |login2-prace.salomon.it4i.cz|2222|gsissh|login2|
- |login3-prace.salomon.it4i.cz|2222|gsissh|login3|
- |login4-prace.salomon.it4i.cz|2222|gsissh|login4|
-
-Â
-
- $ gsissh -p 2222 salomon-prace.it4i.cz
-
-When logging from other PRACE system, the prace_service script can be
-used:
-
- $ gsissh `prace_service -i -s salomon`
-
-Â
-
-***Access from public Internet:**
-
-It is recommended to use the single DNS name
-salomon.it4i.cz which is distributed between
-the two login nodes. If needed, user can login directly to one of the
-login nodes. The addresses are:
-
- |Login address|Port|Protocol|Login node|
- |---|---|
- |salomon.it4i.cz|2222|gsissh|login1, login2, login3 or login4|
- |login1.salomon.it4i.cz|2222|gsissh|login1|
- |login2-prace.salomon.it4i.cz|2222|gsissh|login2|
- |login3-prace.salomon.it4i.cz|2222|gsissh|login3|
- |login4-prace.salomon.it4i.cz|2222|gsissh|login4|
-
- $ gsissh -p 2222 salomon.it4i.cz
-
-When logging from other PRACE system, the
-prace_service script can be used:
-
- $ gsissh `prace_service -e -s salomon`
-
-Â
-
-Although the preferred and recommended file transfer mechanism is [using
-GridFTP](prace.html#file-transfers), the GSI SSH
-implementation on Salomon supports also SCP, so for small files transfer
-gsiscp can be used:
-
- $ gsiscp -P 2222 _LOCAL_PATH_TO_YOUR_FILE_ salomon.it4i.cz:_SALOMON_PATH_TO_YOUR_FILE_
-
- $ gsiscp -P 2222 salomon.it4i.cz:_SALOMON_PATH_TO_YOUR_FILE_ _LOCAL_PATH_TO_YOUR_FILE_
-
- $ gsiscp -P 2222 _LOCAL_PATH_TO_YOUR_FILE_ salomon-prace.it4i.cz:_SALOMON_PATH_TO_YOUR_FILE_
-
- $ gsiscp -P 2222 salomon-prace.it4i.cz:_SALOMON_PATH_TO_YOUR_FILE_ _LOCAL_PATH_TO_YOUR_FILE_
-
-### Access to X11 applications (VNC)
-
-If the user needs to run X11 based graphical application and does not
-have a X11 server, the applications can be run using VNC service. If the
-user is using regular SSH based access, please see the [section in
-general
-documentation](../get-started-with-it4innovations/accessing-the-clusters/graphical-user-interface/x-window-system/x-window-and-vnc.html).
-
-If the user uses GSI SSH based access, then the procedure is similar to
-the SSH based access ([look
-here](../get-started-with-it4innovations/accessing-the-clusters/graphical-user-interface/x-window-system/x-window-and-vnc.html)),
-only the port forwarding must be done using GSI SSH:
-
- $ gsissh -p 2222 salomon.it4i.cz -L 5961:localhost:5961
-
-### Access with SSH
-
-After successful obtainment of login credentials for the local
-IT4Innovations account, the PRACE users can access the cluster as
-regular users using SSH. For more information please see the [section in
-general
-documentation](accessing-the-cluster/shell-and-data-access/shell-and-data-access.html).
-
-File transfers
-------------------
-
-PRACE users can use the same transfer mechanisms as regular users (if
-they've undergone the full registration procedure). For information
-about this, please see [the section in the general
-documentation](accessing-the-cluster/shell-and-data-access/shell-and-data-access.html).
-
-Apart from the standard mechanisms, for PRACE users to transfer data
-to/from Salomon cluster, a GridFTP server running Globus Toolkit GridFTP
-service is available. The service is available from public Internet as
-well as from the internal PRACE network (accessible only from other
-PRACE partners).
-
-There's one control server and three backend servers for striping and/or
-backup in case one of them would fail.
-
-***Access from PRACE network:**
-
- |Login address|Port|Node role|
- |---|---|
- |gridftp-prace.salomon.it4i.cz|2812|Front end /control server|
- |lgw1-prace.salomon.it4i.cz|2813|Backend / data mover server|
- |lgw2-prace.salomon.it4i.cz|2813|Backend / data mover server|
- |lgw3-prace.salomon.it4i.cz|2813|Backend / data mover server|
-
-Copy files **to** Salomon by running the following commands on your
-local machine:
-
- $ globus-url-copy file://_LOCAL_PATH_TO_YOUR_FILE_ gsiftp://gridftp-prace.salomon.it4i.cz:2812/home/prace/_YOUR_ACCOUNT_ON_SALOMON_/_PATH_TO_YOUR_FILE_
-
-Or by using prace_service script:
-
- $ globus-url-copy file://_LOCAL_PATH_TO_YOUR_FILE_ gsiftp://`prace_service -i -f salomon`/home/prace/_YOUR_ACCOUNT_ON_SALOMON_/_PATH_TO_YOUR_FILE_
-
-Copy files **from** Salomon:
-
- $ globus-url-copy gsiftp://gridftp-prace.salomon.it4i.cz:2812/home/prace/_YOUR_ACCOUNT_ON_SALOMON_/_PATH_TO_YOUR_FILE_ file://_LOCAL_PATH_TO_YOUR_FILE_
-
-Or by using prace_service script:
-
- $ globus-url-copy gsiftp://`prace_service -i -f salomon`/home/prace/_YOUR_ACCOUNT_ON_SALOMON_/_PATH_TO_YOUR_FILE_ file://_LOCAL_PATH_TO_YOUR_FILE_
-
-Â
-
-***Access from public Internet:**
-
- |Login address|Port|Node role|
- |---|---|
- |gridftp.salomon.it4i.cz|2812|Front end /control server|
- |lgw1.salomon.it4i.cz|2813|Backend / data mover server|
- |lgw2.salomon.it4i.cz|2813|Backend / data mover server|
- |lgw3.salomon.it4i.cz|2813|Backend / data mover server|
-
-Copy files **to** Salomon by running the following commands on your
-local machine:
-
- $ globus-url-copy file://_LOCAL_PATH_TO_YOUR_FILE_ gsiftp://gridftp.salomon.it4i.cz:2812/home/prace/_YOUR_ACCOUNT_ON_SALOMON_/_PATH_TO_YOUR_FILE_
-
-Or by using prace_service script:
-
- $ globus-url-copy file://_LOCAL_PATH_TO_YOUR_FILE_ gsiftp://`prace_service -e -f salomon`/home/prace/_YOUR_ACCOUNT_ON_SALOMON_/_PATH_TO_YOUR_FILE_
-
-Copy files **from** Salomon:
-
- $ globus-url-copy gsiftp://gridftp.salomon.it4i.cz:2812/home/prace/_YOUR_ACCOUNT_ON_SALOMON_/_PATH_TO_YOUR_FILE_ file://_LOCAL_PATH_TO_YOUR_FILE_
-
-Or by using prace_service script:
-
- $ globus-url-copy gsiftp://`prace_service -e -f salomon`/home/prace/_YOUR_ACCOUNT_ON_SALOMON_/_PATH_TO_YOUR_FILE_ file://_LOCAL_PATH_TO_YOUR_FILE_
-
-Â
-
-Generally both shared file systems are available through GridFTP:
-
- |File system mount point|Filesystem|Comment|
- |---|---|
- |/home|Lustre|Default HOME directories of users in format /home/prace/login/|
- |/scratch|Lustre|Shared SCRATCH mounted on the whole cluster|
-
-More information about the shared file systems is available
-[here](storage.html).
-
-Please note, that for PRACE users a "prace" directory is used also on
-the SCRATCH file system.
-
- |Data type|Default path|
- |---|---|
- |large project files|/scratch/work/user/prace/login/|
- |large scratch/temporary data|/scratch/temp/|
-
-Usage of the cluster
---------------------
-
-There are some limitations for PRACE user when using the cluster. By
-default PRACE users aren't allowed to access special queues in the PBS
-Pro to have high priority or exclusive access to some special equipment
-like accelerated nodes and high memory (fat) nodes. There may be also
-restrictions obtaining a working license for the commercial software
-installed on the cluster, mostly because of the license agreement or
-because of insufficient amount of licenses.
-
-For production runs always use scratch file systems. The available file
-systems are described [here](storage/storage.html).
-
-### Software, Modules and PRACE Common Production Environment
-
-All system wide installed software on the cluster is made available to
-the users via the modules. The information about the environment and
-modules usage is in this [section of general
-documentation](environment-and-modules.html).
-
-PRACE users can use the "prace" module to use the [PRACE Common
-Production
-Environment](http://www.prace-ri.eu/PRACE-common-production).
-
- $ module load prace
-
-Â
-
-### Resource Allocation and Job Execution
-
-General information about the resource allocation, job queuing and job
-execution is in this [section of general
-documentation](resource-allocation-and-job-execution/introduction.html).
-
-For PRACE users, the default production run queue is "qprace". PRACE
-users can also use two other queues "qexp" and "qfree".
-
-
- |queue|Active project|Project resources|Nodes|priority|authorization|walltime |
-
- |---|---|
- |**qexp** \|no|none required|32 nodes, max 8 per user|150|no|1 / 1h|
- \
-
- gt; 0 >1006 nodes, max 86 per job 0 no 24 / 48h> 0 >1006 nodes, max 86 per job 0 no 24 / 48h
- \
-
-
- |**qfree** \|yes|none required|752 nodes, max 86 per job|-1024|no|12 / 12h|
- \
-
-
-qprace**, the PRACE \***: This queue is intended for
-normal production runs. It is required that active project with nonzero
-remaining resources is specified to enter the qprace. The queue runs
-with medium priority and no special authorization is required to use it.
-The maximum runtime in qprace is 48 hours. If the job needs longer time,
-it must use checkpoint/restart functionality.
-
-### Accounting & Quota
-
-The resources that are currently subject to accounting are the core
-hours. The core hours are accounted on the wall clock basis. The
-accounting runs whenever the computational cores are allocated or
-blocked via the PBS Pro workload manager (the qsub command), regardless
-of whether the cores are actually used for any calculation. See [example
-in the general
-documentation](resource-allocation-and-job-execution/resources-allocation-policy.html).
-
-PRACE users should check their project accounting using the [PRACE
-Accounting Tool
-(DART)](http://www.prace-ri.eu/accounting-report-tool/).
-
-Users who have undergone the full local registration procedure
-(including signing the IT4Innovations Acceptable Use Policy) and who
-have received local password may check at any time, how many core-hours
-have been consumed by themselves and their projects using the command
-"it4ifree". Please note that you need to know your user password to use
-the command and that the displayed core hours are "system core hours"
-which differ from PRACE "standardized core hours".
-
-The **it4ifree** command is a part of it4i.portal.clients package,
-located here:
-<https://pypi.python.org/pypi/it4i.portal.clients>
-
- $ it4ifree
- Password:
-     PID  Total Used ...by me Free
- Â Â -------- ------- ------ -------- -------
- Â Â OPEN-0-0 1500000 400644Â Â 225265 1099356
- Â Â DD-13-1 Â Â 10000 2606 2606 7394
-
-Â
-
-By default file system quota is applied. To check the current status of
-the quota (separate for HOME and SCRATCH) use
-
- $ quota
- $ lfs quota -u USER_LOGIN /scratch
-
-If the quota is insufficient, please contact the
-[support](prace.html#help-and-support) and request an
-increase.
-
-Â
-
-Â
-
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+++ /dev/null
@@ -1,434 +0,0 @@
-Capacity computing
-==================
-
-
-
-Introduction
-------------
-
-In many cases, it is useful to submit huge (>100+) number of
-computational jobs into the PBS queue system. Huge number of (small)
-jobs is one of the most effective ways to execute embarrassingly
-parallel calculations, achieving best runtime, throughput and computer
-utilization.
-
-However, executing huge number of jobs via the PBS queue may strain the
-system. This strain may result in slow response to commands, inefficient
-scheduling and overall degradation of performance and user experience,
-for all users. For this reason, the number of jobs is **limited to 100
-per user, 1500 per job array**
-
-Please follow one of the procedures below, in case you wish to schedule
-more than >100 jobs at a time.
-
-- Use [Job arrays](capacity-computing.html#job-arrays)
- when running huge number of
- [multithread](capacity-computing.html#shared-jobscript-on-one-node)
- (bound to one node only) or multinode (multithread across
- several nodes) jobs
-- Use [GNU
- parallel](capacity-computing.html#gnu-parallel) when
- running single core jobs
-- Combine[GNU parallel with Job
- arrays](capacity-computing.html#combining-job-arrays-and-gnu-parallel)Â
- when running huge number of single core jobs
-
-Policy
-------
-
-1. A user is allowed to submit at most 100 jobs. Each job may be [a job
- array](capacity-computing.html#job-arrays).
-2. The array size is at most 1000 subjobs.
-
-Job arrays
---------------
-
-Huge number of jobs may be easily submitted and managed as a job array.
-
-A job array is a compact representation of many jobs, called subjobs.
-The subjobs share the same job script, and have the same values for all
-attributes and resources, with the following exceptions:
-
-- each subjob has a unique index, $PBS_ARRAY_INDEX
-- job Identifiers of subjobs only differ by their indices
-- the state of subjobs can differ (R,Q,...etc.)
-
-All subjobs within a job array have the same scheduling priority and
-schedule as independent jobs.
-Entire job array is submitted through a single qsub command and may be
-managed by qdel, qalter, qhold, qrls and qsig commands as a single job.
-
-### Shared jobscript
-
-All subjobs in job array use the very same, single jobscript. Each
-subjob runs its own instance of the jobscript. The instances execute
-different work controlled by $PBS_ARRAY_INDEX variable.
-
-Example:
-
-Assume we have 900 input files with name beginning with "file" (e. g.
-file001, ..., file900). Assume we would like to use each of these input
-files with program executable myprog.x, each as a separate job.
-
-First, we create a tasklist file (or subjobs list), listing all tasks
-(subjobs) - all input files in our example:
-
-`
-$ find . -name 'file*' > tasklist
-`
-
-Then we create jobscript:
-
-`
-#!/bin/bash
-#PBS -A PROJECT_ID
-#PBS -q qprod
-#PBS -l select=1:ncpus=24,walltime=02:00:00
-
-# change to local scratch directory
-SCR=/scratch/work/user/$USER/$PBS_JOBID
-mkdir -p $SCR ; cd $SCR || exit
-
-# get individual tasks from tasklist with index from PBS JOB ARRAY
-TASK=$(sed -n "${PBS_ARRAY_INDEX}p" $PBS_O_WORKDIR/tasklist)
-
-# copy input file and executable to scratch
-cp $PBS_O_WORKDIR/$TASK input ; cp $PBS_O_WORKDIR/myprog.x .
-
-# execute the calculation
-./myprog.x < input > output
-
-# copy output file to submit directory
-cp output $PBS_O_WORKDIR/$TASK.out
-`
-
-In this example, the submit directory holds the 900 input files,
-executable myprog.x and the jobscript file. As input for each run, we
-take the filename of input file from created tasklist file. We copy the
-input file to scratch /scratch/work/user/$USER/$PBS_JOBID, execute
-the myprog.x and copy the output file back to >the submit
-directory, under the $TASK.out name. The myprog.x runs on one
-node only and must use threads to run in parallel. Be aware, that if the
-myprog.x **is not multithreaded**, then all the **jobs are run as single
-thread programs in sequential** manner. Due to allocation of the whole
-node, the **accounted time is equal to the usage of whole node**, while
-using only 1/24 of the node!
-
-If huge number of parallel multicore (in means of multinode multithread,
-e. g. MPI enabled) jobs is needed to run, then a job array approach
-should also be used. The main difference compared to previous example
-using one node is that the local scratch should not be used (as it's not
-shared between nodes) and MPI or other technique for parallel multinode
-run has to be used properly.
-
-### Submit the job array
-
-To submit the job array, use the qsub -J command. The 900 jobs of the
-[example above](capacity-computing.html#array_example) may
-be submitted like this:
-
-`
-$ qsub -N JOBNAME -J 1-900 jobscript
-506493[].isrv5
-`
-
-In this example, we submit a job array of 900 subjobs. Each subjob will
-run on full node and is assumed to take less than 2 hours (please note
-the #PBS directives in the beginning of the jobscript file, dont'
-forget to set your valid PROJECT_ID and desired queue).
-
-Sometimes for testing purposes, you may need to submit only one-element
-array. This is not allowed by PBSPro, but there's a workaround:
-
-`
-$ qsub -N JOBNAME -J 9-10:2 jobscript
-`
-
-This will only choose the lower index (9 in this example) for
-submitting/running your job.
-
-### Manage the job array
-
-Check status of the job array by the qstat command.
-
-`
-$ qstat -a 506493[].isrv5
-
-isrv5:
- Req'd Req'd Elap
-Job ID Username Queue Jobname SessID NDS TSK Memory Time S Time
---------------- -------- -- |---|---| ------ --- --- ------ ----- - -----
-12345[].dm2 user2 qprod xx 13516 1 24 -- 00:50 B 00:02
-`
-
-The status B means that some subjobs are already running.
-
-Check status of the first 100 subjobs by the qstat command.
-
-`
-$ qstat -a 12345[1-100].isrv5
-
-isrv5:
- Req'd Req'd Elap
-Job ID Username Queue Jobname SessID NDS TSK Memory Time S Time
---------------- -------- -- |---|---| ------ --- --- ------ ----- - -----
-12345[1].isrv5 user2 qprod xx 13516 1 24 -- 00:50 R 00:02
-12345[2].isrv5 user2 qprod xx 13516 1 24 -- 00:50 R 00:02
-12345[3].isrv5 user2 qprod xx 13516 1 24 -- 00:50 R 00:01
-12345[4].isrv5 user2 qprod xx 13516 1 24 -- 00:50 Q --
- . . . . . . . . . . .
- , . . . . . . . . . .
-12345[100].isrv5 user2 qprod xx 13516 1 24 -- 00:50 Q --
-`
-
-Delete the entire job array. Running subjobs will be killed, queueing
-subjobs will be deleted.
-
-`
-$ qdel 12345[].isrv5
-`
-
-Deleting large job arrays may take a while.
-
-Display status information for all user's jobs, job arrays, and subjobs.
-
-`
-$ qstat -u $USER -t
-`
-
-Display status information for all user's subjobs.
-
-`
-$ qstat -u $USER -tJ
-`
-
-Read more on job arrays in the [PBSPro Users
-guide](../../pbspro-documentation.html).
-
-GNU parallel
-----------------
-
-Use GNU parallel to run many single core tasks on one node.
-
-GNU parallel is a shell tool for executing jobs in parallel using one or
-more computers. A job can be a single command or a small script that has
-to be run for each of the lines in the input. GNU parallel is most
-useful in running single core jobs via the queue system on Anselm.
-
-For more information and examples see the parallel man page:
-
-`
-$ module add parallel
-$ man parallel
-`
-
-### GNU parallel jobscript
-
-The GNU parallel shell executes multiple instances of the jobscript
-using all cores on the node. The instances execute different work,
-controlled by the $PARALLEL_SEQ variable.
-
-Example:
-
-Assume we have 101 input files with name beginning with "file" (e. g.
-file001, ..., file101). Assume we would like to use each of these input
-files with program executable myprog.x, each as a separate single core
-job. We call these single core jobs tasks.
-
-First, we create a tasklist file, listing all tasks - all input files in
-our example:
-
-`
-$ find . -name 'file*' > tasklist
-`
-
-Then we create jobscript:
-
-`
-#!/bin/bash
-#PBS -A PROJECT_ID
-#PBS -q qprod
-#PBS -l select=1:ncpus=24,walltime=02:00:00
-
-[ -z "$PARALLEL_SEQ" ] &&
-{ module add parallel ; exec parallel -a $PBS_O_WORKDIR/tasklist $0 ; }
-
-# change to local scratch directory
-SCR=/scratch/work/user/$USER/$PBS_JOBID/$PARALLEL_SEQ
-mkdir -p $SCR ; cd $SCR || exit
-
-# get individual task from tasklist
-TASK=$1 Â
-
-# copy input file and executable to scratch
-cp $PBS_O_WORKDIR/$TASK input
-
-# execute the calculation
-cat input > output
-
-# copy output file to submit directory
-cp output $PBS_O_WORKDIR/$TASK.out
-`
-
-In this example, tasks from tasklist are executed via the GNU
-parallel. The jobscript executes multiple instances of itself in
-parallel, on all cores of the node. Once an instace of jobscript is
-finished, new instance starts until all entries in tasklist are
-processed. Currently processed entry of the joblist may be retrieved via
-$1 variable. Variable $TASK expands to one of the input filenames from
-tasklist. We copy the input file to local scratch, execute the myprog.x
-and copy the output file back to the submit directory, under the
-$TASK.out name.Â
-
-### Submit the job
-
-To submit the job, use the qsub command. The 101 tasks' job of the
-[example above](capacity-computing.html#gp_example) may be
-submitted like this:
-
-`
-$ qsub -N JOBNAME jobscript
-12345.dm2
-`
-
-In this example, we submit a job of 101 tasks. 24 input files will be
-processed in parallel. The 101 tasks on 24 cores are assumed to
-complete in less than 2 hours.
-
-Please note the #PBS directives in the beginning of the jobscript file,
-dont' forget to set your valid PROJECT_ID and desired queue.
-
-Job arrays and GNU parallel
--------------------------------
-
-Combine the Job arrays and GNU parallel for best throughput of single
-core jobs
-
-While job arrays are able to utilize all available computational nodes,
-the GNU parallel can be used to efficiently run multiple single-core
-jobs on single node. The two approaches may be combined to utilize all
-available (current and future) resources to execute single core jobs.
-
-Every subjob in an array runs GNU parallel to utilize all cores on the
-node
-
-### GNU parallel, shared jobscript
-
-Combined approach, very similar to job arrays, can be taken. Job array
-is submitted to the queuing system. The subjobs run GNU parallel. The
-GNU parallel shell executes multiple instances of the jobscript using
-all cores on the node. The instances execute different work, controlled
-by the $PBS_JOB_ARRAY and $PARALLEL_SEQ variables.
-
-Example:
-
-Assume we have 992 input files with name beginning with "file" (e. g.
-file001, ..., file992). Assume we would like to use each of these input
-files with program executable myprog.x, each as a separate single core
-job. We call these single core jobs tasks.
-
-First, we create a tasklist file, listing all tasks - all input files in
-our example:
-
-`
-$ find . -name 'file*' > tasklist
-`
-
-Next we create a file, controlling how many tasks will be executed in
-one subjob
-
-`
-$ seq 32 > numtasks
-`
-
-Then we create jobscript:
-
-`
-#!/bin/bash
-#PBS -A PROJECT_ID
-#PBS -q qprod
-#PBS -l select=1:ncpus=24,walltime=02:00:00
-
-[ -z "$PARALLEL_SEQ" ] &&
-{ module add parallel ; exec parallel -a $PBS_O_WORKDIR/numtasks $0 ; }
-
-# change to local scratch directory
-SCR=/scratch/work/user/$USER/$PBS_JOBID/$PARALLEL_SEQ
-mkdir -p $SCR ; cd $SCR || exit
-
-# get individual task from tasklist with index from PBS JOB ARRAY and index form Parallel
-IDX=$(($PBS_ARRAY_INDEX + $PARALLEL_SEQ - 1))
-TASK=$(sed -n "${IDX}p" $PBS_O_WORKDIR/tasklist)
-[ -z "$TASK" ] && exit
-
-# copy input file and executable to scratch
-cp $PBS_O_WORKDIR/$TASK input
-
-# execute the calculation
-cat input > output
-
-# copy output file to submit directory
-cp output $PBS_O_WORKDIR/$TASK.out
-`
-
-In this example, the jobscript executes in multiple instances in
-parallel, on all cores of a computing node. Variable $TASK expands to
-one of the input filenames from tasklist. We copy the input file to
-local scratch, execute the myprog.x and copy the output file back to the
-submit directory, under the $TASK.out name. The numtasks file controls
-how many tasks will be run per subjob. Once an task is finished, new
-task starts, until the number of tasks in numtasks file is reached.
-
-Select subjob walltime and number of tasks per subjob carefully
-
-Â When deciding this values, think about following guiding rules :
-
-1. Let n=N/24. Inequality (n+1) * T < W should hold. The N is
- number of tasks per subjob, T is expected single task walltime and W
- is subjob walltime. Short subjob walltime improves scheduling and
- job throughput.
-2. Number of tasks should be modulo 24.
-3. These rules are valid only when all tasks have similar task
- walltimes T.
-
-### Submit the job array
-
-To submit the job array, use the qsub -J command. The 992 tasks' job of
-the [example
-above](capacity-computing.html#combined_example) may be
-submitted like this:
-
-`
-$ qsub -N JOBNAME -J 1-992:32 jobscript
-12345[].dm2
-`
-
-In this example, we submit a job array of 31 subjobs. Note the -J
-1-992:**48**, this must be the same as the number sent to numtasks file.
-Each subjob will run on full node and process 24 input files in
-parallel, 48 in total per subjob. Every subjob is assumed to complete
-in less than 2 hours.
-
-Please note the #PBS directives in the beginning of the jobscript file,
-dont' forget to set your valid PROJECT_ID and desired queue.
-
-Examples
---------
-
-Download the examples in
-[capacity.zip](capacity-computing-example),Â
-illustrating the above listed ways to run huge number of jobs. We
-recommend to try out the examples, before using this for running
-production jobs.
-
-Unzip the archive in an empty directory on Anselm and follow the
-instructions in the README file
-
-`
-$ unzip capacity.zip
-$ cd capacity
-$ cat README
-`
-
-Â
-
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-Resource Allocation and Job Execution
-=====================================
-
-
-
-To run a [job](job-submission-and-execution.html),
-[computational
-resources](resources-allocation-policy.html) for this
-particular job must be allocated. This is done via the PBS Pro job
-workload manager software, which efficiently distributes workloads
-across the supercomputer. Extensive informations about PBS Pro can be
-found in the [official documentation
-here](../../pbspro-documentation.html), especially in
-the [PBS Pro User's
-Guide](https://docs.it4i.cz/pbspro-documentation/pbspro-users-guide).
-
-Resources Allocation Policy
----------------------------
-
-The resources are allocated to the job in a fairshare fashion, subject
-to constraints set by the queue and resources available to the Project.
-[The Fairshare](job-priority.html) at Salomon ensures
-that individual users may consume approximately equal amount of
-resources per week. The resources are accessible via several queues for
-queueing the jobs. The queues provide prioritized and exclusive access
-to the computational resources. Following queues are available to Anselm
-users:
-
-- **qexp**, the \
-- **qprod**, the \***
-- **qlong**, the Long queue
-- **qmpp**, the Massively parallel queue
-- **qfat**, the queue to access SMP UV2000 machine
-- **qfree,** the Free resource utilization queue
-
-Check the queue status at <https://extranet.it4i.cz/rsweb/salomon/>
-
-Read more on the [Resource Allocation
-Policy](resources-allocation-policy.html) page.
-
-Job submission and execution
-----------------------------
-
-Use the **qsub** command to submit your jobs.
-
-The qsub submits the job into the queue. The qsub command creates a
-request to the PBS Job manager for allocation of specified resources.Â
-The **smallest allocation unit is entire node, 24 cores**, with
-exception of the qexp queue. The resources will be allocated when
-available, subject to allocation policies and constraints. **After the
-resources are allocated the jobscript or interactive shell is executed
-on first of the allocated nodes.**
-
-Read more on the [Job submission and
-execution](job-submission-and-execution.html) page.
-
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-Job scheduling
-==============
-
-Job execution priority
-----------------------
-
-Scheduler gives each job an execution priority and then uses this job
-execution priority to select which job(s) to run.
-
-Job execution priority is determined by these job properties (in order
-of importance):
-
-1. queue priority
-2. fairshare priority
-3. eligible time
-
-### Queue priority
-
-Queue priority is priority of queue where job is queued before
-execution.
-
-Queue priority has the biggest impact on job execution priority.
-Execution priority of jobs in higher priority queues is always greater
-than execution priority of jobs in lower priority queues. Other
-properties of job used for determining job execution priority (fairshare
-priority, eligible time) cannot compete with queue priority.
-
-Queue priorities can be seen at
-<https://extranet.it4i.cz/rsweb/salomon/queues>
-
-### Fairshare priority
-
-Fairshare priority is priority calculated on recent usage of resources.
-Fairshare priority is calculated per project, all members of project
-share same fairshare priority. Projects with higher recent usage have
-lower fairshare priority than projects with lower or none recent usage.
-
-Fairshare priority is used for ranking jobs with equal queue priority.
-
-Fairshare priority is calculated as
-
-
-
-where MAX_FAIRSHARE has value 1E6,
-usage~Project~ is cumulated usage by all members of selected project,
-usage~Total~ is total usage by all users, by all projects.
-
-Usage counts allocated corehours (ncpus*walltime). Usage is decayed, or
-cut in half periodically, at the interval 168 hours (one week).
-Jobs queued in queue qexp are not calculated to project's usage.
-
-Calculated usage and fairshare priority can be seen at
-<https://extranet.it4i.cz/rsweb/salomon/projects>.
-
-Calculated fairshare priority can be also seen as
-Resource_List.fairshare attribute of a job.
-
-###Eligible time
-
-Eligible time is amount (in seconds) of eligible time job accrued while
-waiting to run. Jobs with higher eligible time gains higher
-priority.
-
-Eligible time has the least impact on execution priority. Eligible time
-is used for sorting jobs with equal queue priority and fairshare
-priority. It is very, very difficult for >eligible time to
-compete with fairshare priority.
-
-Eligible time can be seen as eligible_time attribute of
-job.
-
-### Formula
-
-Job execution priority (job sort formula) is calculated as:
-
-
-
-### Job backfilling
-
-The scheduler uses job backfilling.
-
-Backfilling means fitting smaller jobs around the higher-priority jobs
-that the scheduler is going to run next, in such a way that the
-higher-priority jobs are not delayed. Backfilling allows us to keep
-resources from becoming idle when the top job (job with the highest
-execution priority) cannot run.
-
-The scheduler makes a list of jobs to run in order of execution
-priority. Scheduler looks for smaller jobs that can fit into the usage
-gaps
-around the highest-priority jobs in the list. The scheduler looks in the
-prioritized list of jobs and chooses the highest-priority smaller jobs
-that fit. Filler jobs are run only if they will not delay the start time
-of top jobs.
-
-It means, that jobs with lower execution priority can be run before jobs
-with higher execution priority.
-
-It is **very beneficial to specify the walltime** when submitting jobs.
-
-Specifying more accurate walltime enables better schedulling, better
-execution times and better resource usage. Jobs with suitable (small)
-walltime could be backfilled - and overtake job(s) with higher priority.
-
-### Job placement
-
-Job [placement can be controlled by flags during
-submission](job-submission-and-execution.html#job_placement).
-
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-Job submission and execution
-============================
-
-
-
-Job Submission
---------------
-
-When allocating computational resources for the job, please specify
-
-1. suitable queue for your job (default is qprod)
-2. number of computational nodes required
-3. number of cores per node required
-4. maximum wall time allocated to your calculation, note that jobs
- exceeding maximum wall time will be killed
-5. Project ID
-6. Jobscript or interactive switch
-
-Use the **qsub** command to submit your job to a queue for allocation of
-the computational resources.
-
-Submit the job using the qsub command:
-
-`
-$ qsub -A Project_ID -q queue -l select=x:ncpus=y,walltime=[[hh:]mm:]ss[.ms] jobscript
-`
-
-The qsub submits the job into the queue, in another words the qsub
-command creates a request to the PBS Job manager for allocation of
-specified resources. The resources will be allocated when available,
-subject to above described policies and constraints. **After the
-resources are allocated the jobscript or interactive shell is executed
-on first of the allocated nodes.**
-
-PBS statement nodes (qsub -l nodes=nodespec) is not supported on Salomon
-cluster.**
-
-### Job Submission Examples
-
-`
-$ qsub -A OPEN-0-0 -q qprod -l select=64:ncpus=24,walltime=03:00:00 ./myjob
-`
-
-In this example, we allocate 64 nodes, 24 cores per node, for 3 hours.
-We allocate these resources via the qprod queue, consumed resources will
-be accounted to the Project identified by Project ID OPEN-0-0. Jobscript
-myjob will be executed on the first node in the allocation.
-
-Â
-
-`
-$ qsub -q qexp -l select=4:ncpus=24 -I
-`
-
-In this example, we allocate 4 nodes, 24 cores per node, for 1 hour. We
-allocate these resources via the qexp queue. The resources will be
-available interactively
-
-Â
-
-`
-$ qsub -A OPEN-0-0 -q qlong -l select=10:ncpus=24 ./myjob
-`
-
-In this example, we allocate 10 nodes, 24 cores per node, for 72 hours.
-We allocate these resources via the qlong queue. Jobscript myjob will be
-executed on the first node in the allocation.
-
-Â
-
-`
-$ qsub -A OPEN-0-0 -q qfree -l select=10:ncpus=24 ./myjob
-`
-
-In this example, we allocate 10Â nodes, 24 cores per node, for 12 hours.
-We allocate these resources via the qfree queue. It is not required that
-the project OPEN-0-0 has any available resources left. Consumed
-resources are still accounted for. Jobscript myjob will be executed on
-the first node in the allocation.
-
-### Intel Xeon Phi co-processors
-
-To allocate a node with Xeon Phi co-processor, user needs to specify
-that in select statement. Currently only allocation of whole nodes with
-both Phi cards as the smallest chunk is supported. Standard PBSPro
-approach through attributes "accelerator", "naccelerators" and
-"accelerator_model" is used. The "accelerator_model" can be omitted,
-since on Salomon only one type of accelerator type/model is available.
-
-The absence of specialized queue for accessing the nodes with cards
-means, that the Phi cards can be utilized in any queue, including qexp
-for testing/experiments, qlong for longer jobs, qfree after the project
-resources have been spent, etc. The Phi cards are thus also available to
-PRACE users. There's no need to ask for permission to utilize the Phi
-cards in project proposals.
-
-`
-$ qsub -A OPEN-0-0 -I -q qprod -l select=1:ncpus=24:accelerator=True:naccelerators=2:accelerator_model=phi7120 ./myjob
-`
-
-In this example, we allocate 1 node, with 24 cores, with 2 Xeon Phi
-7120p cards, running batch job ./myjob. The default time for qprod is
-used, e. g. 24 hours.
-
-`
-$ qsub -A OPEN-0-0 -I -q qlong -l select=4:ncpus=24:accelerator=True:naccelerators=2 -l walltime=56:00:00 -I
-`
-
-In this example, we allocate 4 nodes, with 24 cores per node (totalling
-96 cores), with 2 Xeon Phi 7120p cards per node (totalling 8 Phi cards),
-running interactive job for 56 hours. The accelerator model name was
-omitted.
-
-### UV2000 SMP
-
-14 NUMA nodes available on UV2000
-Per NUMA node allocation.
-Jobs are isolated by cpusets.
-
-The UV2000 (node uv1) offers 3328GB of RAM and 112 cores, distributed in
-14 NUMA nodes. A NUMA node packs 8 cores and approx. 236GB RAM. In the
-PBSÂ the UV2000 provides 14 chunks, a chunk per NUMA node (seeÂ
-[Resource allocation
-policy](resources-allocation-policy.html)). The jobs on
-UV2000 are isolated from each other by cpusets, so that a job by one
-user may not utilize CPU or memory allocated to a job by other user.
-Always, full chunks are allocated, a job may only use resources of the
-NUMA nodes allocated to itself.
-
-`
-Â $ qsub -A OPEN-0-0 -q qfat -l select=14 ./myjob
-`
-
-In this example, we allocate all 14 NUMA nodes (corresponds to 14
-chunks), 112 cores of the SGI UV2000 node for 72 hours. Jobscript myjob
-will be executed on the node uv1.
-
-`
-$ qsub -A OPEN-0-0 -q qfat -l select=1:mem=2000GB ./myjob
-`
-
-In this example, we allocate 2000GB of memory on the UV2000 for 72
-hours. By requesting 2000GB of memory, 10 chunks are allocated.
-Jobscript myjob will be executed on the node uv1.
-
-### Useful tricks
-
-All qsub options may be [saved directly into the
-jobscript](job-submission-and-execution.html#PBSsaved). In
-such a case, no options to qsub are needed.
-
-`
-$ qsub ./myjob
-`
-
-Â
-
-By default, the PBS batch system sends an e-mail only when the job is
-aborted. Disabling mail events completely can be done like this:
-
-`
-$ qsub -m n
-`
-
-Advanced job placement
---------------------------
-
-### Placement by name
-
-Specific nodes may be allocated via the PBS
-
-`
-qsub -A OPEN-0-0 -q qprod -l select=1:ncpus=24:host=r24u35n680+1:ncpus=24:host=r24u36n681 -I
-`
-
-Or using short names
-
-`
-qsub -A OPEN-0-0 -q qprod -l select=1:ncpus=24:host=cns680+1:ncpus=24:host=cns681 -I
-`
-
-In this example, we allocate nodes r24u35n680 and r24u36n681, all 24
-cores per node, for 24 hours. Consumed resources will be accounted to
-the Project identified by Project ID OPEN-0-0. The resources will be
-available interactively.
-
-### Placement by |Hypercube|dimension|
-
-Nodes may be selected via the PBS resource attribute ehc_[1-7]d .
-
- |Hypercube|dimension|
- --------------- |---|---|---------------------------------
- |1D|ehc_1d|
- |2D|ehc_2d|
- |3D|ehc_3d|
- |4D|ehc_4d|
- |5D|ehc_5d|
- |6D|ehc_6d|
- |7D|ehc_7d|
-
-Â
-
-`
-$ qsub -A OPEN-0-0 -q qprod -l select=4:ncpus=24 -l place=group=ehc_1d -I
-`
-
-In this example, we allocate 4 nodes, 24 cores, selecting only the nodes
-with [hypercube
-dimension](../network-1/7d-enhanced-hypercube.html) 1.
-
-### Placement by IB switch
-
-Groups of computational nodes are connected to chassis integrated
-Infiniband switches. These switches form the leaf switch layer of the
-[Infiniband network](../network-1.html) . Nodes sharing
-the leaf switch can communicate most efficiently. Sharing the same
-switch prevents hops in the network and provides for unbiased, most
-efficient network communication.
-
-There are at most 9 nodes sharing the same Infiniband switch.
-
-Infiniband switch list:
-
-`
-$ qmgr -c "print node @a" | grep switch
-set node r4i1n11 resources_available.switch = r4i1s0sw1
-set node r2i0n0 resources_available.switch = r2i0s0sw1
-set node r2i0n1 resources_available.switch = r2i0s0sw1
-...
-`
-
-List of all nodes per Infiniband switch:
-
-`
-$ qmgr -c "print node @a" | grep r36sw3
-set node r36u31n964 resources_available.switch = r36sw3
-set node r36u32n965 resources_available.switch = r36sw3
-set node r36u33n966 resources_available.switch = r36sw3
-set node r36u34n967 resources_available.switch = r36sw3
-set node r36u35n968 resources_available.switch = r36sw3
-set node r36u36n969 resources_available.switch = r36sw3
-set node r37u32n970 resources_available.switch = r36sw3
-set node r37u33n971 resources_available.switch = r36sw3
-set node r37u34n972 resources_available.switch = r36sw3
-`
-
-Nodes sharing the same switch may be selected via the PBS resource
-attribute switch.
-
-We recommend allocating compute nodes of a single switch when best
-possible computational network performance is required to run the job
-efficiently:
-
-`
-$ qsub -A OPEN-0-0 -q qprod -l select=9:ncpus=24:switch=r4i1s0sw1 ./myjob
-`
-
-In this example, we request all the 9 nodes sharing the r4i1s0sw1 switch
-for 24 hours.
-
-`
-$ qsub -A OPEN-0-0 -q qprod -l select=9:ncpus=24 -l place=group=switch ./myjob
-`
-
-In this example, we request 9 nodes placed on the same switch using node
-grouping placement for 24 hours.
-
-HTML commented section #1 (turbo boost is to be implemented)
-
-Job Management
---------------
-
-Check status of your jobs using the **qstat** and **check-pbs-jobs**
-commands
-
-`
-$ qstat -a
-$ qstat -a -u username
-$ qstat -an -u username
-$ qstat -f 12345.isrv5
-`
-
-Example:
-
-`
-$ qstat -a
-
-srv11:
- Req'd Req'd Elap
-Job ID Username Queue Jobname SessID NDS TSK Memory Time S Time
---------------- -------- -- |---|---| ------ --- --- ------ ----- - -----
-16287.isrv5 user1 qlong job1 6183 4 64 -- 144:0 R 38:25
-16468.isrv5 user1 qlong job2 8060 4 64 -- 144:0 R 17:44
-16547.isrv5 user2 qprod job3x 13516 2 32 -- 48:00 R 00:58
-`
-
-In this example user1 and user2 are running jobs named job1, job2 and
-job3x. The jobs job1 and job2 are using 4 nodes, 16 cores per node each.
-The job1 already runs for 38 hours and 25 minutes, job2 for 17 hours 44
-minutes. The job1 already consumed 64*38.41 = 2458.6 core hours. The
-job3x already consumed 0.96*32 = 30.93 core hours. These consumed core
-hours will be accounted on the respective project accounts, regardless
-of whether the allocated cores were actually used for computations.
-
-Check status of your jobs using check-pbs-jobs command. Check presence
-of user's PBS jobs' processes on execution hosts. Display load,
-processes. Display job standard and error output. Continuously display
-(tail -f) job standard or error output.
-
-`
-$ check-pbs-jobs --check-all
-$ check-pbs-jobs --print-load --print-processes
-$ check-pbs-jobs --print-job-out --print-job-err
-$ check-pbs-jobs --jobid JOBID --check-all --print-all
-$ check-pbs-jobs --jobid JOBID --tailf-job-out
-`
-
-Examples:
-
-`
-$ check-pbs-jobs --check-all
-JOB 35141.dm2, session_id 71995, user user2, nodes r3i6n2,r3i6n3
-Check session id: OK
-Check processes
-r3i6n2: OK
-r3i6n3: No process
-`
-
-In this example we see that job 35141.dm2 currently runs no process on
-allocated node r3i6n2, which may indicate an execution error.
-
-`
-$ check-pbs-jobs --print-load --print-processes
-JOB 35141.dm2, session_id 71995, user user2, nodes r3i6n2,r3i6n3
-Print load
-r3i6n2: LOAD: 16.01, 16.01, 16.00
-r3i6n3: LOAD: 0.01, 0.00, 0.01
-Print processes
- %CPU CMD
-r3i6n2: 0.0 -bash
-r3i6n2: 0.0 /bin/bash /var/spool/PBS/mom_priv/jobs/35141.dm2.SC
-r3i6n2: 99.7 run-task
-...
-`
-
-In this example we see that job 35141.dm2 currently runs process
-run-task on node r3i6n2, using one thread only, while node r3i6n3 is
-empty, which may indicate an execution error.
-
-`
-$ check-pbs-jobs --jobid 35141.dm2 --print-job-out
-JOB 35141.dm2, session_id 71995, user user2, nodes r3i6n2,r3i6n3
-Print job standard output:
-======================== Job start ==========================
-Started at   : Fri Aug 30 02:47:53 CEST 2013
-Script name  : script
-Run loop 1
-Run loop 2
-Run loop 3
-`
-
-In this example, we see actual output (some iteration loops) of the job
-35141.dm2
-
-Manage your queued or running jobs, using the **qhold**, **qrls**,
-qdel,** **qsig** or **qalter** commands
-
-You may release your allocation at any time, using qdel command
-
-`
-$ qdel 12345.isrv5
-`
-
-You may kill a running job by force, using qsig command
-
-`
-$ qsig -s 9 12345.isrv5
-`
-
-Learn more by reading the pbs man page
-
-`
-$ man pbs_professional
-`
-
-Job Execution
--------------
-
-### Jobscript
-
-Prepare the jobscript to run batch jobs in the PBS queue system
-
-The Jobscript is a user made script, controlling sequence of commands
-for executing the calculation. It is often written in bash, other
-scripts may be used as well. The jobscript is supplied to PBS **qsub**
-command as an argument and executed by the PBS Professional workload
-manager.
-
-The jobscript or interactive shell is executed on first of the allocated
-nodes.
-
-`
-$ qsub -q qexp -l select=4:ncpus=24 -N Name0 ./myjob
-$ qstat -n -u username
-
-isrv5:
- Req'd Req'd Elap
-Job ID Username Queue Jobname SessID NDS TSK Memory Time S Time
---------------- -------- -- |---|---| ------ --- --- ------ ----- - -----
-15209.isrv5 username qexp Name0 5530 4 96 -- 01:00 R 00:00
- r21u01n577/0*24+r21u02n578/0*24+r21u03n579/0*24+r21u04n580/0*24
-`
-
- In this example, the nodes r21u01n577, r21u02n578, r21u03n579,
-r21u04n580 were allocated for 1 hour via the qexp queue. The jobscript
-myjob will be executed on the node r21u01n577, while the
-nodes r21u02n578, r21u03n579, r21u04n580 are available for use as well.
-
-The jobscript or interactive shell is by default executed in home
-directory
-
-`
-$ qsub -q qexp -l select=4:ncpus=24 -I
-qsub: waiting for job 15210.isrv5 to start
-qsub: job 15210.isrv5 ready
-
-$ pwd
-/home/username
-`
-
-In this example, 4 nodes were allocated interactively for 1 hour via the
-qexp queue. The interactive shell is executed in the home directory.
-
-All nodes within the allocation may be accessed via ssh. Unallocated
-nodes are not accessible to user.
-
-The allocated nodes are accessible via ssh from login nodes. The nodes
-may access each other via ssh as well.
-
-Calculations on allocated nodes may be executed remotely via the MPI,
-ssh, pdsh or clush. You may find out which nodes belong to the
-allocation by reading the $PBS_NODEFILE file
-
-`
-qsub -q qexp -l select=2:ncpus=24 -I
-qsub: waiting for job 15210.isrv5 to start
-qsub: job 15210.isrv5 ready
-
-$ pwd
-/home/username
-
-$ sort -u $PBS_NODEFILE
-r2i5n6.ib0.smc.salomon.it4i.cz
-r4i6n13.ib0.smc.salomon.it4i.cz
-r4i7n0.ib0.smc.salomon.it4i.cz
-r4i7n2.ib0.smc.salomon.it4i.cz
-
-
-$ pdsh -w r2i5n6,r4i6n13,r4i7n[0,2] hostname
-r4i6n13: r4i6n13
-r2i5n6: r2i5n6
-r4i7n2: r4i7n2
-r4i7n0: r4i7n0
-`
-
-In this example, the hostname program is executed via pdsh from the
-interactive shell. The execution runs on all four allocated nodes. The
-same result would be achieved if the pdsh is called from any of the
-allocated nodes or from the login nodes.
-
-### Example Jobscript for MPI Calculation
-
-Production jobs must use the /scratch directory for I/O
-
-The recommended way to run production jobs is to change to /scratch
-directory early in the jobscript, copy all inputs to /scratch, execute
-the calculations and copy outputs to home directory.
-
-`
-#!/bin/bash
-
-# change to scratch directory, exit on failure
-SCRDIR=/scratch/work/user/$USER/myjob
-mkdir -p $SCRDIR
-cd $SCRDIR || exit
-
-# copy input file to scratch
-cp $PBS_O_WORKDIR/input .
-cp $PBS_O_WORKDIR/mympiprog.x .
-
-# load the mpi module
-module load OpenMPI
-
-# execute the calculation
-mpiexec -pernode ./mympiprog.x
-
-# copy output file to home
-cp output $PBS_O_WORKDIR/.
-
-#exit
-exit
-`
-
-In this example, some directory on the /home holds the input file input
-and executable mympiprog.x . We create a directory myjob on the /scratch
-filesystem, copy input and executable files from the /home directory
-where the qsub was invoked ($PBS_O_WORKDIR) to /scratch, execute the
-MPI programm mympiprog.x and copy the output file back to the /home
-directory. The mympiprog.x is executed as one process per node, on all
-allocated nodes.
-
-Consider preloading inputs and executables onto [shared
-scratch](../storage.html) before the calculation starts.
-
-In some cases, it may be impractical to copy the inputs to scratch and
-outputs to home. This is especially true when very large input and
-output files are expected, or when the files should be reused by a
-subsequent calculation. In such a case, it is users responsibility to
-preload the input files on shared /scratch before the job submission and
-retrieve the outputs manually, after all calculations are finished.
-
-Store the qsub options within the jobscript.
-Use **mpiprocs** and **ompthreads** qsub options to control the MPI job
-execution.
-
-Example jobscript for an MPI job with preloaded inputs and executables,
-options for qsub are stored within the script :
-
-`
-#!/bin/bash
-#PBS -q qprod
-#PBS -N MYJOB
-#PBS -l select=100:ncpus=24:mpiprocs=1:ompthreads=24
-#PBS -A OPEN-0-0
-
-# change to scratch directory, exit on failure
-SCRDIR=/scratch/work/user/$USER/myjob
-cd $SCRDIR || exit
-
-# load the mpi module
-module load OpenMPI
-
-# execute the calculation
-mpiexec ./mympiprog.x
-
-#exit
-exit
-`
-
-In this example, input and executable files are assumed preloaded
-manually in /scratch/$USER/myjob directory. Note the **mpiprocs** and
-ompthreads** qsub options, controlling behavior of the MPI execution.
-The mympiprog.x is executed as one process per node, on all 100
-allocated nodes. If mympiprog.x implements OpenMP threads, it will run
-24 threads per node.
-
-HTML commented section #2 (examples need to be reworked)
-
-### Example Jobscript for Single Node Calculation
-
-Local scratch directory is often useful for single node jobs. Local
-scratch will be deleted immediately after the job ends.
-Be very careful, use of RAM disk filesystem is at the expense of
-operational memory.
-
-Example jobscript for single node calculation, using [local
-scratch](../storage.html) on the node:
-
-`
-#!/bin/bash
-
-# change to local scratch directory
-cd /lscratch/$PBS_JOBID || exit
-
-# copy input file to scratch
-cp $PBS_O_WORKDIR/input .
-cp $PBS_O_WORKDIR/myprog.x .
-
-# execute the calculation
-./myprog.x
-
-# copy output file to home
-cp output $PBS_O_WORKDIR/.
-
-#exit
-exit
-`
-
-In this example, some directory on the home holds the input file input
-and executable myprog.x . We copy input and executable files from the
-home directory where the qsub was invoked ($PBS_O_WORKDIR) to local
-scratch /lscratch/$PBS_JOBID, execute the myprog.x and copy the output
-file back to the /home directory. The myprog.x runs on one node only and
-may use threads.
-
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-Resources Allocation Policy
-===========================
-
-
-
-Resources Allocation Policy
----------------------------
-
-The resources are allocated to the job in a fairshare fashion, subject
-to constraints set by the queue and resources available to the Project.
-The Fairshare at Anselm ensures that individual users may consume
-approximately equal amount of resources per week. Detailed information
-in the [Job scheduling](job-priority.html) section. The
-resources are accessible via several queues for queueing the jobs. The
-queues provide prioritized and exclusive access to the computational
-resources. Following table provides the queue partitioning overview:
-
-Â
-
- |queue |active project |project resources |nodes<th align="left">min ncpus*<th align="left">priority<th align="left">authorization<th align="left">walltime |
- | --- | --- |
- |<strong>qexp</strong>\ |no |none required |32 nodes, max 8 per user |24 |>150 |no |1 / 1h |
- |<strong>qprod</strong>\ |yes |> 0 |>1006 nodes, max 86 per job\ |24 |0 |no |24 / 48h |
- |<strong>qlong</strong>Long queue |yes |> 0 |256 nodes, max 40 per job, only non-accelerated nodes allowed |24 |0 |no |72 / 144h |
- |<strong>qmpp</strong>Massive parallel queue |yes |> 0 |1006 nodes |24 |0 |yes |2 / 4h |
- |<strong>qfat</strong>UV2000 queue |yes |> 0\ |1 (uv1) |8 |0 |yes |24 / 48h |
- |<strong>qfree</strong>\ |yes |none required |752 nodes, max 86 per job |24 |-1024 |no |12 / 12h |
- |<strong><strong>qviz</strong></strong>Visualization queue |yes |none required |2 (with NVIDIA Quadro K5000) |4 |150 |no |1 / 2h |
-
-Â
-
-The qfree queue is not free of charge**. [Normal
-accounting](resources-allocation-policy.html#resources-accounting-policy)
-applies. However, it allows for utilization of free resources, once a
-Project exhausted all its allocated computational resources. This does
-not apply for Directors Discreation's projects (DD projects) by default.
-Usage of qfree after exhaustion of DD projects computational resources
-is allowed after request for this queue.
-
-Â
-
-- **qexp**, the \: This queue is dedicated for testing and
- running very small jobs. It is not required to specify a project to
- enter the qexp. >*>There are 2 nodes always reserved for
- this queue (w/o accelerator), maximum 8 nodes are available via the
- qexp for a particular user. *The nodes may be
- allocated on per core basis. No special authorization is required to
- use it. The maximum runtime in qexp is 1 hour.
-- **qprod**, the \***: This queue is intended for
- normal production runs. It is required that active project with
- nonzero remaining resources is specified to enter the qprod. All
- nodes may be accessed via the qprod queue, however only 86 per job.
- ** Full nodes, 24 cores per node are allocated. The queue runs with
- medium priority and no special authorization is required to use it.
- The maximum runtime in qprod is 48 hours.
-- **qlong**, the Long queue***: This queue is intended for long
- production runs. It is required that active project with nonzero
- remaining resources is specified to enter the qlong. Only 336 nodes
- without acceleration may be accessed via the qlong queue. Full
- nodes, 24 cores per node are allocated. The queue runs with medium
- priority and no special authorization is required to use it.>
- *The maximum runtime in qlong is 144 hours (three times of the
- standard qprod time - 3 * 48 h)*
-- >***qmpp**, the massively parallel queue. This queue is
- intended for massively parallel runs. It is required that active
- project with nonzero remaining resources is specified to enter
- the qmpp. All nodes may be accessed via the qmpp queue. ** Full
- nodes, 24 cores per node are allocated. The queue runs with medium
- priority and no special authorization is required to use it. The
- maximum runtime in qmpp is 4 hours. An PI> *needs explicitly*
- ask [support](https://support.it4i.cz/rt/)
- for authorization to enter the queue for all users associated to
- her/his Project.
-
-- >***qfat**, the UV2000 queue. This queue is dedicated
- to access the fat SGI UV2000 SMP machine. The machine (uv1) has 112
- Intel IvyBridge cores at 3.3GHz and 3.25TB RAM. An PI> *needs
- explicitly* ask
- [support](https://support.it4i.cz/rt/) for
- authorization to enter the queue for all users associated to her/his
- Project.***
-- **qfree**, the \***: The queue qfree is intended
- for utilization of free resources, after a Project exhausted all its
- allocated computational resources (Does not apply to DD projects
- by default. DD projects have to request for persmission on qfree
- after exhaustion of computational resources.). It is required that
- active project is specified to enter the queue, however no remaining
- resources are required. Consumed resources will be accounted to
- the Project. Only 178 nodes without accelerator may be accessed from
- this queue. Full nodes, 24 cores per node are allocated. The queue
- runs with very low priority and no special authorization is required
- to use it. The maximum runtime in qfree is 12 hours.
-- **qviz**, the Visualization queue***: Intended for
- pre-/post-processing using OpenGL accelerated graphics. Currently
- when accessing the node, each user gets 4 cores of a CPU allocated,
- thus approximately 73 GB of RAM and 1/7 of the GPU capacity
- (default "chunk"). *If more GPU power or RAM is required, it is
- recommended to allocate more chunks (with 4 cores each) up to one
- whole node per user, so that all 28 cores, 512 GB RAM and whole GPU
- is exclusive. This is currently also the maximum allowed allocation
- per one user. One hour of work is allocated by default, the user may
- ask for 2 hours maximum.*
-
-Â
-
-To access node with Xeon Phi co-processor user needs to specify that in
-[job submission select
-statement](job-submission-and-execution.html).
-
-### Notes
-
-The job wall clock time defaults to **half the maximum time**, see table
-above. Longer wall time limits can be [set manually, see
-examples](job-submission-and-execution.html).
-
-Jobs that exceed the reserved wall clock time (Req'd Time) get killed
-automatically. Wall clock time limit can be changed for queuing jobs
-(state Q) using the qalter command, however can not be changed for a
-running job (state R).
-
-Salomon users may check current queue configuration at
-<https://extranet.it4i.cz/rsweb/salomon/queues>.
-
-### Queue status
-
-Check the status of jobs, queues and compute nodes at
-[https://extranet.it4i.cz/rsweb/salomon/](https://extranet.it4i.cz/rsweb/salomon)
-
-Â
-
-
-
-Â
-
-Display the queue status on Salomon:
-
-`
-$ qstat -q
-`
-
-The PBS allocation overview may be obtained also using the rspbs
-command.
-
-`
-$ rspbs
-Usage: rspbs [options]
-
-Options:
- --version            show program's version number and exit
- -h, --help           show this help message and exit
- --get-server-details Print server
- --get-queues         Print queues
- --get-queues-details Print queues details
- --get-reservations   Print reservations
-Â --get-reservations-details
-Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Print reservations details
- --get-nodes          Print nodes of PBS complex
- --get-nodeset        Print nodeset of PBS complex
- --get-nodes-details  Print nodes details
- --get-jobs           Print jobs
- --get-jobs-details   Print jobs details
-Â --get-jobs-check-params
-Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Print jobid, job state, session_id, user, nodes
- --get-users          Print users of jobs
-Â --get-allocated-nodes
-Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Print allocated nodes of jobs
-Â --get-allocated-nodeset
-Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Print allocated nodeset of jobs
- --get-node-users     Print node users
- --get-node-jobs      Print node jobs
- --get-node-ncpus     Print number of ncpus per node
-Â --get-node-allocated-ncpus
-Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Print number of allocated ncpus per node
- --get-node-qlist     Print node qlist
- --get-node-ibswitch  Print node ibswitch
- --get-user-nodes     Print user nodes
- --get-user-nodeset   Print user nodeset
- --get-user-jobs      Print user jobs
- --get-user-jobc      Print number of jobs per user
- --get-user-nodec     Print number of allocated nodes per user
- --get-user-ncpus     Print number of allocated ncpus per user
- --get-qlist-nodes    Print qlist nodes
- --get-qlist-nodeset  Print qlist nodeset
- --get-ibswitch-nodes Print ibswitch nodes
-Â --get-ibswitch-nodeset
-Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Print ibswitch nodeset
- --summary            Print summary
-Â --get-node-ncpu-chart
-Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Obsolete. Print chart of allocated ncpus per node
-Â --server=SERVERÂ Â Â Â Â Â Use given PBS server
-Â --state=STATEÂ Â Â Â Â Â Â Â Only for given job state
-Â --jobid=JOBIDÂ Â Â Â Â Â Â Â Only for given job ID
-Â --user=USERÂ Â Â Â Â Â Â Â Â Â Only for given user
-Â --node=NODEÂ Â Â Â Â Â Â Â Â Â Only for given node
-Â --nodestate=NODESTATE
-Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Only for given node state (affects only --get-node*
-Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â --get-qlist-* --get-ibswitch-* actions)
- --incl-finished      Include finished jobs
-`
-
-Resources Accounting Policy
--------------------------------
-
-### The Core-Hour
-
-The resources that are currently subject to accounting are the
-core-hours. The core-hours are accounted on the wall clock basis. The
-accounting runs whenever the computational cores are allocated or
-blocked via the PBS Pro workload manager (the qsub command), regardless
-of whether the cores are actually used for any calculation. 1 core-hour
-is defined as 1 processor core allocated for 1 hour of wall clock time.
-Allocating a full node (24 cores) for 1 hour accounts to 24 core-hours.
-See example in the [Job submission and
-execution](job-submission-and-execution.html) section.
-
-### Check consumed resources
-
-The **it4ifree** command is a part of it4i.portal.clients package,
-located here:
-<https://pypi.python.org/pypi/it4i.portal.clients>
-
-User may check at any time, how many core-hours have been consumed by
-himself/herself and his/her projects. The command is available on
-clusters' login nodes.
-
-`
-$ it4ifree
-Password:
-    PID  Total Used ...by me Free
-Â Â -------- ------- ------ -------- -------
-Â Â OPEN-0-0 1500000 400644Â Â 225265 1099356
-Â Â DD-13-1 Â Â 10000 2606 2606 7394
-`
-
-Â
-
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-ANSYS CFX
-=========
-
-[ANSYS
-CFX](http://www.ansys.com/Products/Simulation+Technology/Fluid+Dynamics/Fluid+Dynamics+Products/ANSYS+CFX)
-software is a high-performance, general purpose fluid dynamics program
-that has been applied to solve wide-ranging fluid flow problems for over
-20 years. At the heart of ANSYS CFX is its advanced solver technology,
-the key to achieving reliable and accurate solutions quickly and
-robustly. The modern, highly parallelized solver is the foundation for
-an abundant choice of physical models to capture virtually any type of
-phenomena related to fluid flow. The solver and its many physical models
-are wrapped in a modern, intuitive, and flexible GUI and user
-environment, with extensive capabilities for customization and
-automation using session files, scripting and a powerful expression
-language.
-
-To run ANSYS CFX in batch mode you can utilize/modify the default
-cfx.pbs script and execute it via the qsub command.
-
- #!/bin/bash
- #PBS -l nodes=2:ppn=24
- #PBS -q qprod
- #PBS -N $USER-CFX-Project
- #PBS -A OPEN-0-0
-
- #! Mail to user when job terminate or abort
- #PBS -m ae
-
- #!change the working directory (default is home directory)
- #cd <working directory> (working directory must exists)
- WORK_DIR="/scratch/work/user/$USER"
- cd $WORK_DIR
-
- echo Running on host `hostname`
- echo Time is `date`
- echo Directory is `pwd`
- echo This jobs runs on the following processors:
- echo `cat $PBS_NODEFILE`
-
- module load ANSYS
-
- #### Set number of processors per host listing
- procs_per_host=24
- #### Create host list
- hl=""
- for host in `cat $PBS_NODEFILE`
- do
- if [ "$hl" = "" ]
- then hl="$host:$procs_per_host"
- else hl="${hl}:$host:$procs_per_host"
- fi
- done
-
- echo Machines: $hl
-
- # prevent ANSYS from attempting to use scif0 interface
- export MPI_IC_ORDER="UDAPL"
-
- #-dev input.def includes the input of CFX analysis in DEF format
- #-P the name of prefered license feature (aa_r=ANSYS Academic Research, ane3fl=Multiphysics(commercial))
- cfx5solve -def input.def -size 4 -size-ni 4x -part-large -start-method "Platform MPI Distributed Parallel" -par-dist $hl -P aa_r
-
-Header of the pbs file (above) is common and description can be findÂ
-[this
-site](../../resource-allocation-and-job-execution/job-submission-and-execution.html).
-SVS FEM recommends to utilize sources by keywords: nodes, ppn. These
-keywords allows to address directly the number of nodes (computers) and
-cores (ppn) which will be utilized in the job. Also the rest of code
-assumes such structure of allocated resources.
-
-Working directory has to be created before sending pbs job into the
-queue. Input file should be in working directory or full path to input
-file has to be specified. >Input file has to be defined by common
-CFX def file which is attached to the cfx solver via parameter
--def
-
-License**Â should be selected by parameter -P (Big letter **P**).
-Licensed products are the following: aa_r
-(ANSYS **Academic Research), ane3fl (ANSYS
-Multiphysics)-**Commercial.
-[More about licensing here](licensing.html)
-
-Â We have observed that the -P settings does not always work. Please set
-your [license
-preferences](setting-license-preferences.html) instead.
-
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-ANSYS Fluent
-============
-
-[ANSYS
-Fluent](http://www.ansys.com/Products/Simulation+Technology/Fluid+Dynamics/Fluid+Dynamics+Products/ANSYS+Fluent)
-software contains the broad physical modeling capabilities needed to
-model flow, turbulence, heat transfer, and reactions for industrial
-applications ranging from air flow over an aircraft wing to combustion
-in a furnace, from bubble columns to oil platforms, from blood flow to
-semiconductor manufacturing, and from clean room design to wastewater
-treatment plants. Special models that give the software the ability to
-model in-cylinder combustion, aeroacoustics, turbomachinery, and
-multiphase systems have served to broaden its reach.
-
-1. Common way to run Fluent over pbs file
-------------------------------------------------------
-
-To run ANSYS Fluent in batch mode you can utilize/modify the
-default fluent.pbs script and execute it via the qsub command.
-
- #!/bin/bash
- #PBS -S /bin/bash
- #PBS -l nodes=2:ppn=24
- #PBS -q qprod
- #PBS -N Fluent-Project
- #PBS -A OPEN-0-0
-
- #! Mail to user when job terminate or abort
- #PBS -m ae
-
- #!change the working directory (default is home directory)
- #cd <working directory> (working directory must exists)
- WORK_DIR="/scratch/work/user/$USER"
- cd $WORK_DIR
-
- echo Running on host `hostname`
- echo Time is `date`
- echo Directory is `pwd`
- echo This jobs runs on the following processors:
- echo `cat $PBS_NODEFILE`
-
- #### Load ansys module so that we find the cfx5solve command
- module load ANSYS
-
- # Use following line to specify MPI for message-passing instead
- NCORES=`wc -l $PBS_NODEFILE |awk '{print $1}'`
-
- /apps/cae/ANSYS/16.1/v161/fluent/bin/fluent 3d -t$NCORES -cnf=$PBS_NODEFILE -g -i fluent.jou
-
-Header of the pbs file (above) is common and description can be find on
-[this
-site](../../resource-allocation-and-job-execution/job-submission-and-execution.html).
-[SVS FEM](http://www.svsfem.cz) recommends to utilize
-sources by keywords: nodes, ppn. These keywords allows to address
-directly the number of nodes (computers) and cores (ppn) which will be
-utilized in the job. Also the rest of code assumes such structure of
-allocated resources.
-
-Working directory has to be created before sending pbs job into the
-queue. Input file should be in working directory or full path to input
-file has to be specified. Input file has to be defined by common Fluent
-journal file which is attached to the Fluent solver via parameter -i
-fluent.jou
-
-Journal file with definition of the input geometry and boundary
-conditions and defined process of solution has e.g. the following
-structure:
-
- /file/read-case aircraft_2m.cas.gz
- /solve/init
- init
- /solve/iterate
- 10
- /file/write-case-dat aircraft_2m-solution
- /exit yes
-
-The appropriate dimension of the problem has to be set by
-parameter (2d/3d).Â
-
-2. Fast way to run Fluent from command line
---------------------------------------------------------
-
- fluent solver_version [FLUENT_options] -i journal_file -pbs
-
-This syntax will start the ANSYS FLUENT job under PBS Professional using
-the qsub command in a batch manner. When
-resources are available, PBS Professional will start the job and return
-a job ID, usually in the form of
-*job_ID.hostname*. This job ID can then be used
-to query, control, or stop the job using standard PBS Professional
-commands, such as qstat or
-qdel. The job will be run out of the current
-working directory, and all output will be written to the file
-fluent.o>Â
-*job_ID*. Â Â Â Â
-
-3. Running Fluent via user's config file
-----------------------------------------
-
-The sample script uses a configuration file called
-pbs_fluent.conf  if no command line arguments
-are present. This configuration file should be present in the directory
-from which the jobs are submitted (which is also the directory in which
-the jobs are executed). The following is an example of what the content
-of pbs_fluent.conf can be:
-
-`
- input="example_small.flin"
- case="Small-1.65m.cas"
- fluent_args="3d -pmyrinet"
- outfile="fluent_test.out"
- mpp="true"
-`
-
-The following is an explanation of the parameters:
-
- input is the name of the input
-file.
-
- case is the name of the
-.cas file that the input file will utilize.
-
- fluent_args are extra ANSYS FLUENT
-arguments. As shown in the previous example, you can specify the
-interconnect by using the -p interconnect
-command. The available interconnects include
-ethernet (the default),
-myrinet, class="monospace">
-infiniband, vendor,
-altix>, and
-crayx. The MPI is selected automatically, based
-on the specified interconnect.
-
- outfile is the name of the file to which
-the standard output will be sent.
-
- mpp="true" will tell the job script to
-execute the job across multiple processors. Â Â Â Â Â Â Â Â
-
-To run ANSYS Fluent in batch mode with user's config file you can
-utilize/modify the following script and execute it via the qsub
-command.
-
-`
-#!/bin/sh
-#PBS -l nodes=2:ppn=24
-#PBS -1 qprod
-#PBS -N Fluent-Project
-#PBS -A OPEN-0-0
-
- cd $PBS_O_WORKDIR
-
- #We assume that if they didn’t specify arguments then they should use the
- #config file if [ "xx${input}${case}${mpp}${fluent_args}zz" = "xxzz" ]; then
- if [ -f pbs_fluent.conf ]; then
- . pbs_fluent.conf
- else
- printf "No command line arguments specified, "
- printf "and no configuration file found. Exiting n"
- fi
- fi
-
-
- #Augment the ANSYS FLUENT command line arguments case "$mpp" in
- true)
- #MPI job execution scenario
- num_nodes=â€cat $PBS_NODEFILE | sort -u | wc -lâ€
- cpus=â€expr $num_nodes * $NCPUSâ€
- #Default arguments for mpp jobs, these should be changed to suit your
- #needs.
- fluent_args="-t${cpus} $fluent_args -cnf=$PBS_NODEFILE"
- ;;
- *)
- #SMP case
- #Default arguments for smp jobs, should be adjusted to suit your
- #needs.
- fluent_args="-t$NCPUS $fluent_args"
- ;;
- esac
- #Default arguments for all jobs
- fluent_args="-ssh -g -i $input $fluent_args"
-
- echo "---------- Going to start a fluent job with the following settings:
- Input: $input
- Case: $case
- Output: $outfile
- Fluent arguments: $fluent_args"
-
- #run the solver
- /apps/cae/ANSYS/16.1/v161/fluent/bin/fluent $fluent_args > $outfile
-`
-
-It runs the jobs out of the directory from which they are
-submitted (PBS_O_WORKDIR).
-
-4. Running Fluent in parralel
------------------------------
-
-Fluent could be run in parallel only under Academic Research
-license. To do so this ANSYS Academic Research license must be placed
-before ANSYS CFD license in user preferences. To make this change
-[anslic_admin utility should be
-run](setting-license-preferences.html).
-
-Â
-
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-ANSYS LS-DYNA
-=============
-
-[ANSYS
-LS-DYNA](http://www.ansys.com/Products/Simulation+Technology/Structural+Mechanics/Explicit+Dynamics/ANSYS+LS-DYNA)
-software provides convenient and easy-to-use access to the
-technology-rich, time-tested explicit solver without the need to contend
-with the complex input requirements of this sophisticated program.
-Introduced in 1996, ANSYS LS-DYNA capabilities have helped customers in
-numerous industries to resolve highly intricate design
-issues. >ANSYS Mechanical users have been able take advantage of
-complex explicit solutions for a long time utilizing the traditional
-ANSYS Parametric Design Language (APDL) environment. >These
-explicit capabilities are available to ANSYS Workbench users as well.
-The Workbench platform is a powerful, comprehensive, easy-to-use
-environment for engineering simulation. CAD import from all sources,
-geometry cleanup, automatic meshing, solution, parametric optimization,
-result visualization and comprehensive report generation are all
-available within a single fully interactive modern graphical user
-environment.
-
-To run ANSYS LS-DYNA in batch mode you can utilize/modify the
-default ansysdyna.pbs script and execute it via the qsub command.
-
- #!/bin/bash
- #PBS -l nodes=2:ppn=24
- #PBS -q qprod
- #PBS -N DYNA-Project
- #PBS -A OPEN-0-0
-
- #! Mail to user when job terminate or abort
- #PBS -m ae
-
- #!change the working directory (default is home directory)
- #cd <working directory>
- WORK_DIR="/scratch/work/user/$USER"
- cd $WORK_DIR
-
- echo Running on host `hostname`
- echo Time is `date`
- echo Directory is `pwd`
- echo This jobs runs on the following processors:
- echo `cat $PBS_NODEFILE`
-
- module load ANSYS
-
- #### Set number of processors per node
- procs_per_host=24
- #### Create host list
- hl=""
- for host in `cat $PBS_NODEFILE`
- do
- if [ "$hl" = "" ]
- then hl="$host:$procs_per_host"
- else hl="${hl}:$host:$procs_per_host"
- fi
- done
-
- echo Machines: $hl
-
- # prevent ANSYS from attempting to use scif0 interface
- export MPI_IC_ORDER="UDAPL"
-
- lsdyna161 -dis -usessh -machines "$hl" i=input.k
-
-Header of the pbs file (above) is common and description can be
-find > on [this
-site](../../resource-allocation-and-job-execution/job-submission-and-execution.html).
-[SVS FEM](http://www.svsfem.cz) recommends to utilize
-sources by keywords: nodes, ppn. These keywords allows to address
-directly the number of nodes (computers) and cores (ppn) which will be
-utilized in the job. Also the rest of code assumes such structure of
-allocated resources.
-
-Working directory has to be created before sending pbs job into the
-queue. Input file should be in working directory or full path to input
-file has to be specified. Input file has to be defined by common LS-DYNA
-.**k** file which is attached to the ansys solver via parameter i=
-
-Without setting environment variable MPI_IC_ORDER="UDAPL", ANSYS will
-fail to run on nodes with Xeon Phi accelerator (it will use the virtual
-interface of Phi cards instead of the real InfiniBand interface and MPI
-will fail.
-
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-ANSYS MAPDL
-===========
-
-**[ANSYS
-Multiphysics](http://www.ansys.com/Products/Simulation+Technology/Structural+Mechanics/ANSYS+Multiphysics)**
-software offers a comprehensive product solution for both multiphysics
-and single-physics analysis. The product includes structural, thermal,
-fluid and both high- and low-frequency electromagnetic analysis. The
-product also contains solutions for both direct and sequentially coupled
-physics problems including direct coupled-field elements and the ANSYS
-multi-field solver.
-
-To run ANSYS MAPDL in batch mode you can utilize/modify the
-default mapdl.pbs script and execute it via the qsub command.
-
- #!/bin/bash
- #PBS -l nodes=2:ppn=24
- #PBS -q qprod
- #PBS -N ANSYS-Project
- #PBS -A OPEN-0-0
-
- #! Mail to user when job terminate or abort
- #PBS -m ae
-
- #!change the working directory (default is home directory)
- #cd <working directory> (working directory must exists)
- WORK_DIR="/scratch/work/user/$USER"
- cd $WORK_DIR
-
- echo Running on host `hostname`
- echo Time is `date`
- echo Directory is `pwd`
- echo This jobs runs on the following processors:
- echo `cat $PBS_NODEFILE`
-
- module load ANSYS/16.1
-
- #### Set number of processors per host listing
- procs_per_host=24
- #### Create host list
- hl=""
- for host in `cat $PBS_NODEFILE`
- do
- if [ "$hl" = "" ]
- then hl="$host:$procs_per_host"
- else hl="${hl}:$host:$procs_per_host"
- fi
- done
-
- echo Machines: $hl
-
- # prevent ANSYS from attempting to use scif0 interface
- export MPI_IC_ORDER="UDAPL"
-
- #-i input.dat includes the input of analysis in APDL format
- #-o file.out is output file from ansys where all text outputs will be redirected
- #-p the name of license feature (aa_r=ANSYS Academic Research, ane3fl=Multiphysics(commercial), aa_r_dy=Academic AUTODYN)
- ansys161 -b -dis -usessh -p aa_r -i input.dat -o file.out -machines "$hl" -dir $WORK_DIR
-
-Header of the PBS file (above) is common and description can be find on
-[this
-site](../../resource-allocation-and-job-execution/job-submission-and-execution.html).
-[SVS FEM](http://www.svsfem.cz) recommends to utilize
-sources by keywords: nodes, ppn. These keywords allows to address
-directly the number of nodes (computers) and cores (ppn) which will be
-utilized in the job. Also the rest of code assumes such structure of
-allocated resources.
-
-Working directory has to be created before sending pbs job into the
-queue. Input file should be in working directory or full path to input
-file has to be specified. Input file has to be defined by common APDL
-file which is attached to the ansys solver via parameter -i
-
-License** should be selected by parameter -p. Licensed products are
-the following: aa_r (ANSYS **Academic Research), ane3fl (ANSYS
-Multiphysics)-**Commercial**, aa_r_dy (ANSYS **Academic
-AUTODYN)>
-[More about licensing here](licensing.html)
-
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--- a/converted/docs.it4i.cz/salomon/software/ansys/ansys-products-mechanical-fluent-cfx-mapdl.md
+++ /dev/null
@@ -1,31 +0,0 @@
-Overview of ANSYS Products
-==========================
-
-[SVS FEM](http://www.svsfem.cz/)** as **[ANSYS
-Channel partner](http://www.ansys.com/)**Â for Czech
-Republic provided all ANSYS licenses for all clusters and supports of
-all ANSYS Products (Multiphysics, Mechanical, MAPDL, CFX, Fluent,
-Maxwell, LS-DYNA...) to IT staff and ANSYS users. If you are challenging
-to problem of ANSYS functionality contact
-please [hotline@svsfem.cz](mailto:hotline@svsfem.cz?subject=Ostrava%20-%20ANSELM)
-
-The clusters provides as commercial as academic variants. Academic
-variants are distinguished by "**Academic...**" word in the name of
-Â license or by two letter preposition "**aa_**" in the license feature
-name. Change of license is realized on command line respectively
-directly in user's pbs file (see individual products). [More about
-licensing here](licensing.html)
-
-To load the latest version of any ANSYS product (Mechanical, Fluent,
-CFX, MAPDL,...) load the module:
-
- $ module load ANSYS
-
-ANSYS supports interactive regime, but due to assumed solution of
-extremely difficult tasks it is not recommended.
-
-If user needs to work in interactive regime we recommend to configure
-the RSM service on the client machine which allows to forward the
-solution to the clusters directly from the client's Workbench project
-(see ANSYS RSM service).
-
diff --git a/converted/docs.it4i.cz/salomon/software/ansys/ansys-products-mechanical-fluent-cfx-mapdl.pdf b/converted/docs.it4i.cz/salomon/software/ansys/ansys-products-mechanical-fluent-cfx-mapdl.pdf
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--- a/converted/docs.it4i.cz/salomon/software/ansys/ansys.md
+++ /dev/null
@@ -1,31 +0,0 @@
-Overview of ANSYS Products
-==========================
-
-[SVS FEM](http://www.svsfem.cz/)** as **[ANSYS
-Channel partner](http://www.ansys.com/)**Â for Czech
-Republic provided all ANSYS licenses for all clusters and supports of
-all ANSYS Products (Multiphysics, Mechanical, MAPDL, CFX, Fluent,
-Maxwell, LS-DYNA...) to IT staff and ANSYS users. If you are challenging
-to problem of ANSYS functionality contact
-please [hotline@svsfem.cz](mailto:hotline@svsfem.cz?subject=Ostrava%20-%20ANSELM)
-
-The clusters provides as commercial as academic variants. Academic
-variants are distinguished by "**Academic...**" word in the name of
-Â license or by two letter preposition "**aa_**" in the license feature
-name. Change of license is realized on command line respectively
-directly in user's pbs file (see individual products). [More about
-licensing here](ansys/licensing.html)
-
-To load the latest version of any ANSYS product (Mechanical, Fluent,
-CFX, MAPDL,...) load the module:
-
- $ module load ANSYS
-
-ANSYS supports interactive regime, but due to assumed solution of
-extremely difficult tasks it is not recommended.
-
-If user needs to work in interactive regime we recommend to configure
-the RSM service on the client machine which allows to forward the
-solution to the clusters directly from the client's Workbench project
-(see ANSYS RSM service).
-
diff --git a/converted/docs.it4i.cz/salomon/software/ansys/ansys.pdf b/converted/docs.it4i.cz/salomon/software/ansys/ansys.pdf
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--- a/converted/docs.it4i.cz/salomon/software/ansys/licensing.md
+++ /dev/null
@@ -1,45 +0,0 @@
-Licensing and Available Versions
-================================
-
-ANSYS licence can be used by:
------------------------------
-
-- all persons in the carrying out of the CE IT4Innovations Project (In
- addition to the primary licensee, which is VSB - Technical
- University of Ostrava, users are CE IT4Innovations third parties -
- CE IT4Innovations project partners, particularly the University of
- Ostrava, the Brno University of Technology - Faculty of Informatics,
- the Silesian University in Opava, Institute of Geonics AS CR.)
-- all
- persons who have a valid
- license
-- students
- of the Technical University
-
-ANSYS Academic Research
------------------------
-
-The licence intended to be used for science and research, publications,
-students’ projects (academic licence).
-
-ANSYS COM
----------
-
-The licence intended to be used for science and research, publications,
-students’ projects, commercial research with no commercial use
-restrictions.
-
-Available Versions
-------------------
-
-- 16.1
-- 17.0
-
-License Preferences
--------------------
-
-Please [see this page to set license
-preferences](setting-license-preferences.html).
-
-Â
-
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--- a/converted/docs.it4i.cz/salomon/software/ansys/setting-license-preferences.md
+++ /dev/null
@@ -1,34 +0,0 @@
-Setting license preferences
-===========================
-
-Some ANSYS tools allow you to explicitly specify usage of academic or
-commercial licenses in the command line (eg.Â
-ansys161 -p aa_r to select Academic Research
-license). However, we have observed that not all tools obey this option
-and choose commercial license.
-
-Thus you need to configure preferred license order with ANSLIC_ADMIN.
-Please follow these steps and move Academic Research license to the  top
-or bottom of the list accordingly.
-
-Launch the ANSLIC_ADMIN utility in a graphical environment:
-
- $ANSYSLIC_DIR/lic_admin/anslic_admin
-
-ANSLIC_ADMIN Utility will be run
-
-
-
-
-
-
-
-Â
-
-ANSYS Academic Research license should be moved up to the top or down to
-the bottom of the list.
-
-Â
-
-
-
diff --git a/converted/docs.it4i.cz/salomon/software/ansys/setting-license-preferences.pdf b/converted/docs.it4i.cz/salomon/software/ansys/setting-license-preferences.pdf
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--- a/converted/docs.it4i.cz/salomon/software/ansys/workbench.md
+++ /dev/null
@@ -1,74 +0,0 @@
-Workbench
-=========
-
-Workbench Batch Mode
---------------------
-
-It is possible to run Workbench scripts in batch mode. You need to
-configure solvers of individual components to run in parallel mode. Open
-your project in Workbench. Then, for example, in Mechanical, go to Tools
-- Solve Process Settings ..., click Advanced button as shown on the
-screenshot.
-
-
-
-Enable Distribute Solution checkbox and enter number of cores (eg. 48 to
-run on two Salomon nodes). If you want the job to run on more then 1
-node, you must also provide a so called MPI appfile. In the Additional
-Command Line Arguments input field, enter :
-
- -mpifile /path/to/my/job/mpifile.txt
-
-Where /path/to/my/job is the directory where your project is saved. We
-will create the file mpifile.txt programatically later in the batch
-script. For more information, refer to *ANSYS Mechanical APDL Parallel
-Processing* *Guide*.
-
-Now, save the project and close Workbench. We will use this script to
-launch the job:
-
- #!/bin/bash
- #PBS -l select=2:ncpus=24
- #PBS -q qprod
- #PBS -N test9_mpi_2
- #PBS -A OPEN-0-0
-
- # Mail to user when job terminate or abort
- #PBS -m a
-
- # change the working directory
- WORK_DIR="$PBS_O_WORKDIR"
- cd $WORK_DIR
-
- echo Running on host `hostname`
- echo Time is `date`
- echo Directory is `pwd`
- echo This jobs runs on the following nodes:
- echo `cat $PBS_NODEFILE`
-
- module load ANSYS
-
- #### Set number of processors per host listing
- procs_per_host=24
- #### Create MPI appfile
- echo -n "" > mpifile.txt
- for host in `cat $PBS_NODEFILE`
- do
- echo "-h $host -np $procs_per_host $ANSYS160_DIR/bin/ansysdis161 -dis" > mpifile.txt
- done
-
- #-i input.dat includes the input of analysis in APDL format
- #-o file.out is output file from ansys where all text outputs will be redirected
- #-p the name of license feature (aa_r=ANSYS Academic Research, ane3fl=Multiphysics(commercial), aa_r_dy=Academic AUTODYN)
-
- # prevent using scsif0 interface on accelerated nodes
- export MPI_IC_ORDER="UDAPL"
- # spawn remote process using SSH (default is RSH)
- export MPI_REMSH="/usr/bin/ssh"
-
- runwb2 -R jou6.wbjn -B -F test9.wbpj
-
-The solver settings are saved in file solvehandlers.xml, which is not
-located in the project directory. Verify your solved settings when
-uploading a project from your local computer.
-
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--- a/converted/docs.it4i.cz/salomon/software/chemistry/molpro.md
+++ /dev/null
@@ -1,91 +0,0 @@
-Molpro
-======
-
-Molpro is a complete system of ab initio programs for molecular
-electronic structure calculations.
-
-About Molpro
-------------
-
-Molpro is a software package used for accurate ab-initio quantum
-chemistry calculations. More information can be found at the [official
-webpage](http://www.molpro.net/).
-
-License
--------
-
-Molpro software package is available only to users that have a valid
-license. Please contact support to enable access to Molpro if you have a
-valid license appropriate for running on our cluster (eg. >academic
-research group licence, parallel execution).
-
-To run Molpro, you need to have a valid license token present in
-" $HOME/.molpro/token". You can
-download the token from [Molpro
-website](https://www.molpro.net/licensee/?portal=licensee).
-
-Installed version
------------------
-
-Currently on Salomon is installed version 2010.1, patch level 57,
-parallel version compiled with Intel compilers and Intel MPI.
-
-Compilation parameters are default :
-
- |Parameter|Value|
- ------------------------------------------- |---|---|-------------------
- |max number of atoms|200|
- |max number of valence orbitals|300|
- |max number of basis functions|4095|
- |max number of states per symmmetry|20|
- |max number of state symmetries|16|
- |max number of records|200|
- |max number of primitives|maxbfn x [2]|
-
-Â
-
-Running
--------
-
-Molpro is compiled for parallel execution using MPI and OpenMP. By
-default, Molpro reads the number of allocated nodes from PBS and
-launches a data server on one node. On the remaining allocated nodes,
-compute processes are launched, one process per node, each with 16
-threads. You can modify this behavior by using -n, -t and helper-server
-options. Please refer to the [Molpro
-documentation](http://www.molpro.net/info/2010.1/doc/manual/node9.html)
-for more details.Â
-
-The OpenMP parallelization in Molpro is limited and has been observed to
-produce limited scaling. We therefore recommend to use MPI
-parallelization only. This can be achieved by passing option
-mpiprocs=24:ompthreads=1 to PBS.
-
-You are advised to use the -d option to point to a directory in [SCRATCH
-filesystem](../../storage.html). Molpro can produce a
-large amount of temporary data during its run, and it is important that
-these are placed in the fast scratch filesystem.
-
-### Example jobscript
-
- #PBS -A IT4I-0-0
- #PBS -q qprod
- #PBS -l select=1:ncpus=24:mpiprocs=24:ompthreads=1
-
- cd $PBS_O_WORKDIR
-
- # load Molpro module
- module add Molpro/2010.1-patch-57-intel2015b
-
- # create a directory in the SCRATCH filesystem
- mkdir -p /scratch/work/user/$USER/$PBS_JOBID
-
- # copy an example input
- cp /apps/all/Molpro/2010.1-patch57/molprop_2010_1_Linux_x86_64_i8/examples/caffeine_opt_diis.com .
-
- # run Molpro with default options
- molpro -d /scratch/work/user/$USER/$PBS_JOBID caffeine_opt_diis.com
-
- # delete scratch directory
- rm -rf /scratch/work/user/$USER/$PBS_JOBIDÂ
-
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--- a/converted/docs.it4i.cz/salomon/software/chemistry/nwchem.md
+++ /dev/null
@@ -1,66 +0,0 @@
-NWChem
-======
-
-High-Performance Computational Chemistry
-
-Introduction
--------------------------
-
-NWChem aims to provide its users with computational chemistry
-tools that are scalable both in their ability to treat large scientific
-computational chemistry problems efficiently, and in their use of
-available parallel computing resources from high-performance parallel
-supercomputers to conventional workstation clusters.
-
-[Homepage](http://www.nwchem-sw.org/index.php/Main_Page)
-
-Installed versions
-------------------
-
-The following versions are currently installed :Â
-
-- >NWChem/6.3.revision2-2013-10-17-Python-2.7.8, current release.
- Compiled with Intel compilers, MKL and Intel MPI
-
- Â
-
-- >NWChem/6.5.revision26243-intel-2015b-2014-09-10-Python-2.7.8
-
-For a current list of installed versions, execute :Â
-
- module avail NWChem
-
-The recommend to use version 6.5. Version 6.3 fails on Salomon nodes
-with accelerator, because it attempts to communicate over scif0
-interface. In 6.5 this is avoided by
-setting ARMCI_OPENIB_DEVICE=mlx4_0, this setting is included in the
-module.
-
-Running
--------
-
-NWChem is compiled for parallel MPI execution. Normal procedure for MPI
-jobs applies. Sample jobscript :
-
- #PBS -A IT4I-0-0
- #PBS -q qprod
- #PBS -l select=1:ncpus=24:mpiprocs=24
-
- cd $PBS_O_WORKDIR
- module add NWChem/6.5.revision26243-intel-2015b-2014-09-10-Python-2.7.8
- mpirun nwchem h2o.nw
-
-Options
---------------------
-
-Please refer to [the
-documentation](http://www.nwchem-sw.org/index.php/Release62:Top-level)Â and
-in the input file set the following directives :
-
-- >MEMORY : controls the amount of memory NWChem will use
-- >SCRATCH_DIR : set this to a directory in [SCRATCH
- filesystem](../../storage.html)Â (or run the
- calculation completely in a scratch directory). For certain
- calculations, it might be advisable to reduce I/O by forcing
- "direct" mode, eg. "scf direct"
-
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--- a/converted/docs.it4i.cz/salomon/software/chemistry/phono3py.md
+++ /dev/null
@@ -1,202 +0,0 @@
-Phono3py
-========
-
-
-
-Â Introduction
--------------
-
-This GPL software calculates phonon-phonon interactions via the third
-order force constants. It allows to obtain lattice thermal conductivity,
-phonon lifetime/linewidth, imaginary part of self energy at the lowest
-order, joint density of states (JDOS) and weighted-JDOS. For details see
-Phys. Rev. B 91, 094306 (2015) and
-http://atztogo.github.io/phono3py/index.html
-
-Load the phono3py/0.9.14-ictce-7.3.5-Python-2.7.9 module
-
-`
-$ module load phono3py/0.9.14-ictce-7.3.5-Python-2.7.9
-`
-
-Example of calculating thermal conductivity of Si using VASP code.
-------------------------------------------------------------------
-
-### Calculating force constants
-
-One needs to calculate second order and third order force constants
-using the diamond structure of silicon stored in
-[POSCAR](phono3py-input/poscar-si)Â (the same form as in
-VASP) using single displacement calculations within supercell.
-
-`
-$ cat POSCAR
-Â Si
-Â Â 1.0
-Â Â Â Â 5.4335600309153529Â Â Â 0.0000000000000000Â Â Â 0.0000000000000000
-Â Â Â Â 0.0000000000000000Â Â Â 5.4335600309153529Â Â Â 0.0000000000000000
-Â Â Â Â 0.0000000000000000Â Â Â 0.0000000000000000Â Â Â 5.4335600309153529
-Â Si
-Â Â 8
-Direct
-Â Â 0.8750000000000000Â 0.8750000000000000Â 0.8750000000000000
-Â Â 0.8750000000000000Â 0.3750000000000000Â 0.3750000000000000
-Â Â 0.3750000000000000Â 0.8750000000000000Â 0.3750000000000000
-Â Â 0.3750000000000000Â 0.3750000000000000Â 0.8750000000000000
-Â Â 0.1250000000000000Â 0.1250000000000000Â 0.1250000000000000
-Â Â 0.1250000000000000Â 0.6250000000000000Â 0.6250000000000000
-Â Â 0.6250000000000000Â 0.1250000000000000Â 0.6250000000000000
-Â Â 0.6250000000000000Â 0.6250000000000000Â 0.1250000000000000
-`
-
-### Generating displacement using 2x2x2 supercell for both second and third order force constants
-
-`
-$ phono3py -d --dim="2 2 2" -c POSCAR
-`
-
- 111 displacements is created stored in
-disp_fc3.yaml, and the structure input files with this
-displacements are POSCAR-00XXX, where the XXX=111.
-
-`
-disp_fc3.yaml POSCAR-00008 POSCAR-00017 POSCAR-00026 POSCAR-00035 POSCAR-00044 POSCAR-00053 POSCAR-00062 POSCAR-00071 POSCAR-00080 POSCAR-00089 POSCAR-00098 POSCAR-00107
-POSCARÂ Â Â Â Â Â Â Â POSCAR-00009Â POSCAR-00018Â POSCAR-00027Â POSCAR-00036Â POSCAR-00045Â POSCAR-00054Â POSCAR-00063Â POSCAR-00072Â POSCAR-00081Â POSCAR-00090Â POSCAR-00099Â POSCAR-00108
-POSCAR-00001Â Â POSCAR-00010Â POSCAR-00019Â POSCAR-00028Â POSCAR-00037Â POSCAR-00046Â POSCAR-00055Â POSCAR-00064Â POSCAR-00073Â POSCAR-00082Â POSCAR-00091Â POSCAR-00100Â POSCAR-00109
-POSCAR-00002Â Â POSCAR-00011Â POSCAR-00020Â POSCAR-00029Â POSCAR-00038Â POSCAR-00047Â POSCAR-00056Â POSCAR-00065Â POSCAR-00074Â POSCAR-00083Â POSCAR-00092Â POSCAR-00101Â POSCAR-00110
-POSCAR-00003Â Â POSCAR-00012Â POSCAR-00021Â POSCAR-00030Â POSCAR-00039Â POSCAR-00048Â POSCAR-00057Â POSCAR-00066Â POSCAR-00075Â POSCAR-00084Â POSCAR-00093Â POSCAR-00102Â POSCAR-00111
-POSCAR-00004Â Â POSCAR-00013Â POSCAR-00022Â POSCAR-00031Â POSCAR-00040Â POSCAR-00049Â POSCAR-00058Â POSCAR-00067Â POSCAR-00076Â POSCAR-00085Â POSCAR-00094Â POSCAR-00103
-POSCAR-00005Â Â POSCAR-00014Â POSCAR-00023Â POSCAR-00032Â POSCAR-00041Â POSCAR-00050Â POSCAR-00059Â POSCAR-00068Â POSCAR-00077Â POSCAR-00086Â POSCAR-00095Â POSCAR-00104
-POSCAR-00006Â Â POSCAR-00015Â POSCAR-00024Â POSCAR-00033Â POSCAR-00042Â POSCAR-00051Â POSCAR-00060Â POSCAR-00069Â POSCAR-00078Â POSCAR-00087Â POSCAR-00096Â POSCAR-00105
-POSCAR-00007Â Â POSCAR-00016Â POSCAR-00025Â POSCAR-00034Â POSCAR-00043Â POSCAR-00052Â POSCAR-00061Â POSCAR-00070Â POSCAR-00079Â POSCAR-00088Â POSCAR-00097Â POSCAR-00106
-`
-
- For each displacement the forces needs to be
-calculated, i.e. in form of the output file of VASP (vasprun.xml). For a
-single VASP calculations one needs
-[KPOINTS](phono3py-input/KPOINTS),
-[POTCAR](phono3py-input/POTCAR),
-[INCAR](phono3py-input/INCAR) in your case directory
-(where you have POSCARS) and those 111 displacements calculations can be
-generated by [prepare.sh](phono3py-input/prepare.sh)
-script. Then each of the single 111 calculations is submitted
-[run.sh](phono3py-input/run.sh) by
-[submit.sh](phono3py-input/submit.sh).
-
-`
-$./prepare.sh
-$ls
-disp-00001Â disp-00009Â disp-00017Â disp-00025Â disp-00033Â disp-00041Â disp-00049Â disp-00057Â disp-00065Â disp-00073Â disp-00081Â disp-00089Â disp-00097Â disp-00105Â Â Â Â INCAR
-disp-00002Â disp-00010Â disp-00018Â disp-00026Â disp-00034Â disp-00042Â disp-00050Â disp-00058Â disp-00066Â disp-00074Â disp-00082Â disp-00090Â disp-00098Â disp-00106Â Â Â Â KPOINTS
-disp-00003Â disp-00011Â disp-00019Â disp-00027Â disp-00035Â disp-00043Â disp-00051Â disp-00059Â disp-00067Â disp-00075Â disp-00083Â disp-00091Â disp-00099Â disp-00107Â Â Â Â POSCAR
-disp-00004Â disp-00012Â disp-00020Â disp-00028Â disp-00036Â disp-00044Â disp-00052Â disp-00060Â disp-00068Â disp-00076Â disp-00084Â disp-00092Â disp-00100Â disp-00108Â Â Â Â POTCAR
-disp-00005Â disp-00013Â disp-00021Â disp-00029Â disp-00037Â disp-00045Â disp-00053Â disp-00061Â disp-00069Â disp-00077Â disp-00085Â disp-00093Â disp-00101Â disp-00109Â Â Â Â prepare.sh
-disp-00006Â disp-00014Â disp-00022Â disp-00030Â disp-00038Â disp-00046Â disp-00054Â disp-00062Â disp-00070Â disp-00078Â disp-00086Â disp-00094Â disp-00102Â disp-00110Â Â Â Â run.sh
-disp-00007Â disp-00015Â disp-00023Â disp-00031Â disp-00039Â disp-00047Â disp-00055Â disp-00063Â disp-00071Â disp-00079Â disp-00087Â disp-00095Â disp-00103Â disp-00111Â Â Â Â submit.sh
-disp-00008Â disp-00016Â disp-00024Â disp-00032Â disp-00040Â disp-00048Â disp-00056Â disp-00064Â disp-00072Â disp-00080Â disp-00088Â disp-00096Â disp-00104Â disp_fc3.yaml
-`
-
- Taylor your run.sh script to fit into your project and
-other needs and submit all 111 calculations using submit.sh
-script
-
-`
-$ ./submit.sh
-`
-
- Collecting results and post-processing with phono3py
----------------------------------------------------------------------------
-
- Once all jobs are finished and vasprun.xml is created in
-each disp-XXXXX directory the collection is done by
-
-`
-$ phono3py --cf3 disp-{00001..00111}/vasprun.xml
-`
-
- and
-`disp_fc2.yaml, FORCES_FC2`, `FORCES_FC3`{.docutils
-.literal} and disp_fc3.yaml should appear and put into the hdf
-format by
-
-`
-$ phono3py --dim="2 2 2" -c POSCAR
-`
-
-resulting in `fc2.hdf5` and `fc3.hdf5`{.docutils
-.literal}
-
-### Thermal conductivity
-
- The phonon lifetime calculations takes some time,
-however is independent on grid points, so could be splitted:
-
-`
-$ phono3py --fc3 --fc2 --dim="2 2 2" --mesh="9 9 9" --sigma 0.1 --wgp
-`
-
-### Inspecting ir_grid_points.yaml
-
-`
-$ grep grid_point ir_grid_points.yaml
-num_reduced_ir_grid_points: 35
-ir_grid_points:Â # [address, weight]
-- grid_point: 0
-- grid_point: 1
-- grid_point: 2
-- grid_point: 3
-- grid_point: 4
-- grid_point: 10
-- grid_point: 11
-- grid_point: 12
-- grid_point: 13
-- grid_point: 20
-- grid_point: 21
-- grid_point: 22
-- grid_point: 30
-- grid_point: 31
-- grid_point: 40
-- grid_point: 91
-- grid_point: 92
-- grid_point: 93
-- grid_point: 94
-- grid_point: 101
-- grid_point: 102
-- grid_point: 103
-- grid_point: 111
-- grid_point: 112
-- grid_point: 121
-- grid_point: 182
-- grid_point: 183
-- grid_point: 184
-- grid_point: 192
-- grid_point: 193
-- grid_point: 202
-- grid_point: 273
-- grid_point: 274
-- grid_point: 283
-- grid_point: 364
-`
-
-one finds which grid points needed to be calculated, for instance using
-following
-
-`
-$ phono3py --fc3 --fc2 --dim="2 2 2" --mesh="9 9 9" -c POSCARÂ --sigma 0.1 --br --write-gamma --gp="0 1 2
-`
-
- one calculates grid points 0, 1, 2. To automize one can
-use for instance scripts to submit 5 points in series, see
-[gofree-cond1.sh](phono3py-input/gofree-cond1.sh)
-
-`
-$ qsub gofree-cond1.sh
-`
-
- Finally the thermal conductivity result is produced by
-grouping single conductivity per grid calculations usingÂ
-
-`
-$ phono3py --fc3 --fc2 --dim="2 2 2" --mesh="9 9 9" --br --read_gamma
-`
-
diff --git a/converted/docs.it4i.cz/salomon/software/chemistry/phono3py.pdf b/converted/docs.it4i.cz/salomon/software/chemistry/phono3py.pdf
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-Compilers
-=========
-
-Available compilers, including GNU, INTEL and UPC compilers
-
-
-
-There are several compilers for different programming languages
-available on the cluster:
-
-- C/C++
-- Fortran 77/90/95/HPF
-- Unified Parallel C
-- Java
-
-The C/C++ and Fortran compilers are provided by:
-
-Opensource:
-
-- GNU GCC
-- Clang/LLVM
-
-Commercial licenses:
-
-- Intel
-- PGI
-
-Intel Compilers
----------------
-
-For information about the usage of Intel Compilers and other Intel
-products, please read the [Intel Parallel
-studio](intel-suite.html) page.
-
-PGI Compilers
--------------
-
-The Portland Group Cluster Development Kit (PGI CDK) is available.
-
- $ module load PGI
- $ pgcc -v
- $ pgc++ -v
- $ pgf77 -v
- $ pgf90 -v
- $ pgf95 -v
- $ pghpf -v
-
-The PGI CDK also incudes tools for debugging and profiling.
-
-PGDBG OpenMP/MPI debugger and PGPROF OpenMP/MPI profiler are available
-
- $ module load PGI
- $ module load Java
- $ pgdbg &
- $ pgprof &
-
-For more information, see the [PGI
-page](http://www.pgroup.com/products/pgicdk.htm).
-
-GNU
----
-
-For compatibility reasons there are still available the original (old
-4.4.7-11) versions of GNU compilers as part of the OS. These are
-accessible in the search path by default.
-
-It is strongly recommended to use the up to date version which comes
-with the module GCC:
-
- $ module load GCC
- $ gcc -v
- $ g++ -v
- $ gfortran -v
-
-With the module loaded two environment variables are predefined. One for
-maximum optimizations on the cluster's architecture, and the other for
-debugging purposes:
-
- $ echo $OPTFLAGS
- -O3 -march=native
-
- $ echo $DEBUGFLAGS
- -O0 -g
-
-For more information about the possibilities of the compilers, please
-see the man pages.
-
-Unified Parallel C
-------------------
-
-UPC is supported by two compiler/runtime implementations:
-
-- GNU - SMP/multi-threading support only
-- Berkley - multi-node support as well as SMP/multi-threading support
-
-### GNU UPC Compiler
-
-To use the GNU UPC compiler and run the compiled binaries use the module
-gupc
-
- $ module add gupc
- $ gupc -v
- $ g++ -v
-
-Simple program to test the compiler
-
- $ cat count.upc
-
- /* hello.upc - a simple UPC example */
- #include <upc.h>
- #include <stdio.h>
-
- int main() {
- Â if (MYTHREAD == 0) {
- Â Â Â printf("Welcome to GNU UPC!!!n");
- Â }
- Â upc_barrier;
- Â printf(" - Hello from thread %in", MYTHREAD);
- Â return 0;
- }
-
-To compile the example use
-
- $ gupc -o count.upc.x count.upc
-
-To run the example with 5 threads issue
-
- $ ./count.upc.x -fupc-threads-5
-
-For more informations see the man pages.
-
-### Berkley UPC Compiler
-
-To use the Berkley UPC compiler and runtime environment to run the
-binaries use the module bupc
-
- $ module add BerkeleyUPC/2.16.2-gompi-2015b
- $ upcc -version
-
-As default UPC network the "smp" is used. This is very quick and easy
-way for testing/debugging, but limited to one node only.
-
-For production runs, it is recommended to use the native Infiband
-implementation of UPC network "ibv". For testing/debugging using
-multiple nodes, the "mpi" UPC network is recommended. Please note, that
-the selection of the network is done at the compile time** and not at
-runtime (as expected)!
-
-Example UPC code:
-
- $ cat hello.upc
-
- /* hello.upc - a simple UPC example */
- #include <upc.h>
- #include <stdio.h>
-
- int main() {
- Â if (MYTHREAD == 0) {
- Â Â Â printf("Welcome to Berkeley UPC!!!n");
- Â }
- Â upc_barrier;
- Â printf(" - Hello from thread %in", MYTHREAD);
- Â return 0;
- }
-
-To compile the example with the "ibv" UPC network use
-
- $ upcc -network=ibv -o hello.upc.x hello.upc
-
-To run the example with 5 threads issue
-
- $ upcrun -n 5 ./hello.upc.x
-
-To run the example on two compute nodes using all 48 cores, with 48
-threads, issue
-
- $ qsub -I -q qprod -A PROJECT_ID -l select=2:ncpus=24
- $ module add bupc
- $ upcrun -n 48 ./hello.upc.x
-
-Â For more informations see the man pages.
-
-Java
-----
-
-For information how to use Java (runtime and/or compiler), please read
-the [Java page](java.html).
-
-nVidia CUDA
-
-For information how to work with nVidia CUDA, please read the [nVidia
-CUDA
-page](../../anselm-cluster-documentation/software/nvidia-cuda.html).
-
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-COMSOL Multiphysics®
-====================
-
-
-
-Introduction
-
--------------------------
-
-[COMSOL](http://www.comsol.com)
-is a powerful environment for modelling and solving various engineering
-and scientific problems based on partial differential equations. COMSOL
-is designed to solve coupled or multiphysics phenomena. For many
-standard engineering problems COMSOL provides add-on products such as
-electrical, mechanical, fluid flow, and chemical
-applications.
-
-- >[Structural Mechanics
- Module](http://www.comsol.com/structural-mechanics-module),
-
-
-- >[Heat Transfer
- Module](http://www.comsol.com/heat-transfer-module),
-
-
-- >[CFD
- Module](http://www.comsol.com/cfd-module),
-
-
-- >[Acoustics
- Module](http://www.comsol.com/acoustics-module),
-
-
-- >and [many
- others](http://www.comsol.com/products)
-
-COMSOL also allows an
-interface support for
-equation-based modelling of
-partial differential
-equations.
-
-Execution
-
-----------------------
-
-On the clusters COMSOL is available in the latest stable
-version. There are two variants of the release:
-
-- >**Non commercial** or so
- called >**EDU
- variant**>, which can be used for research
- and educational purposes.
-
-- >**Commercial** or so called
- >**COM variant**,
- which can used also for commercial activities.
- >**COM variant**
- has only subset of features compared to the
- >**EDU
- variant**> available.
-
- More about
- licensing will be posted here
- soon.
-
-
-To load the of COMSOL load the module
-
-`
-$ module load COMSOL/51-EDU
-`
-
-By default the **EDU
-variant**> will be loaded. If user needs other
-version or variant, load the particular version. To obtain the list of
-available versions use
-
-`
-$ module avail COMSOL
-`
-
-If user needs to prepare COMSOL jobs in the interactive mode
-it is recommend to use COMSOL on the compute nodes via PBS Pro
-scheduler. In order run the COMSOL Desktop GUI on Windows is recommended
-to use the [Virtual Network Computing
-(VNC)](../../../get-started-with-it4innovations/accessing-the-clusters/graphical-user-interface/x-window-system/x-window-and-vnc.html).
-
-`
-$ xhost +
-$ qsub -I -X -A PROJECT_ID -q qprod -l select=1:ppn=24
-$ module load COMSOL
-$ comsol
-`
-
-To run COMSOL in batch mode, without the COMSOL Desktop GUI
-environment, user can utilized the default (comsol.pbs) job script and
-execute it via the qsub command.
-
-`
-#!/bin/bash
-#PBS -l select=3:ppn=24
-#PBS -q qprod
-#PBS -N JOB_NAME
-#PBS -AÂ PROJECT_ID
-
-cd /scratch/work/user/$USER/ || exit
-
-echo Time is `date`
-echo Directory is `pwd`
-echo '**PBS_NODEFILE***START*******'
-cat $PBS_NODEFILE
-echo '**PBS_NODEFILE***END*********'
-
-text_nodes < cat $PBS_NODEFILE
-
-module load COMSOL
-# module load COMSOL/51-EDU
-
-ntask=$(wc -l $PBS_NODEFILE)
-
-comsol -nn ${ntask} batch -configuration /tmp –mpiarg –rmk –mpiarg pbs -tmpdir /scratch/$USER/ -inputfile name_input_f.mph -outputfile name_output_f.mph -batchlog name_log_f.log
-`
-
-Working directory has to be created before sending the
-(comsol.pbs) job script into the queue. Input file (name_input_f.mph)
-has to be in working directory or full path to input file has to be
-specified. The appropriate path to the temp directory of the job has to
-be set by command option (-tmpdir).
-
-LiveLink™* *for MATLAB®^
--------------------------
-
-COMSOL is the software package for the numerical solution of
-the partial differential equations. LiveLink for MATLAB allows
-connection to the
-COMSOL>><span><span><span><span>**®**</span>^
-API (Application Programming Interface) with the benefits of the
-programming language and computing environment of the MATLAB.
-
-LiveLink for MATLAB is available in both
-**EDU** and
-**COM**
-**variant** of the
-COMSOL release. On the clusters 1 commercial
-(>**COM**) license
-and the 5 educational
-(>**EDU**) licenses
-of LiveLink for MATLAB (please see the [ISV
-Licenses](../isv_licenses.html)) are available.
-Following example shows how to start COMSOL model from MATLAB via
-LiveLink in the interactive mode.
-
-`
-$ xhost +
-$ qsub -I -X -A PROJECT_ID -q qexp -l select=1:ppn=24
-$ module load MATLAB
-$ module load COMSOL
-$ comsol server MATLAB
-`
-
-At the first time to launch the LiveLink for MATLAB
-(client-MATLAB/server-COMSOL connection) the login and password is
-requested and this information is not requested again.
-
-To run LiveLink for MATLAB in batch mode with
-(comsol_matlab.pbs) job script you can utilize/modify the following
-script and execute it via the qsub command.
-
-`
-#!/bin/bash
-#PBS -l select=3:ppn=24
-#PBS -q qprod
-#PBS -N JOB_NAME
-#PBS -AÂ PROJECT_ID
-
-cd /scratch/work/user/$USER || exit
-
-echo Time is `date`
-echo Directory is `pwd`
-echo '**PBS_NODEFILE***START*******'
-cat $PBS_NODEFILE
-echo '**PBS_NODEFILE***END*********'
-
-text_nodes < cat $PBS_NODEFILE
-
-module load MATLAB
-module load COMSOL/51-EDU
-
-ntask=$(wc -l $PBS_NODEFILE)
-
-comsol -nn ${ntask} server -configuration /tmp -mpiarg -rmk -mpiarg pbs -tmpdir /scratch/work/user/$USER/work &
-cd /apps/cae/COMSOL/51/mli
-matlab -nodesktop -nosplash -r "mphstart; addpath /scratch/work/user/$USER/work; test_job"
-`
-
-This example shows how to run Livelink for MATLAB with following
-configuration: 3 nodes and 16 cores per node. Working directory has to
-be created before submitting (comsol_matlab.pbs) job script into the
-queue. Input file (test_job.m) has to be in working directory or full
-path to input file has to be specified. The Matlab command option (-r
-”mphstart”) created a connection with a COMSOL server using the default
-port number.
-
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-Licensing and Available Versions
-================================
-
-Comsol licence can be used by:
-------------------------------
-
-- all persons in the carrying out of the CE IT4Innovations Project (In
- addition to the primary licensee, which is VSB - Technical
- University of Ostrava, users are CE IT4Innovations third parties -
- CE IT4Innovations project partners, particularly the University of
- Ostrava, the Brno University of Technology - Faculty of Informatics,
- the Silesian University in Opava, Institute of Geonics AS CR.)
-- all
- persons who have a valid
- license
-- students
- of the Technical University
-
-Comsol EDU Network Licence
---------------------------
-
-The licence intended to be used for science and research, publications,
-students’ projects, teaching (academic licence).
-
-Comsol COM Network Licence
---------------------------
-
-The licence intended to be used for science and research, publications,
-students’ projects, commercial research with no commercial use
-restrictions. > E
-nables the solution
-of at least one job
-by one user in one
-program start.
-
-Available Versions
-------------------
-
-- ver. 51
-
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-Debuggers and profilers summary
-===============================
-
-
-
-Introduction
-------------
-
-We provide state of the art programms and tools to develop, profile and
-debug HPC codes at IT4Innovations.
-On these pages, we provide an overview of the profiling and debugging
-tools available on Anslem at IT4I.
-
-Intel debugger
---------------
-
-Intel debugger is no longer available since Parallel Studio version 2015
-
-The intel debugger version 13.0 is available, via module intel. The
-debugger works for applications compiled with C and C++ compiler and the
-ifort fortran 77/90/95 compiler. The debugger provides java GUI
-environment. Use [X
-display](../../get-started-with-it4innovations/accessing-the-clusters/graphical-user-interface/x-window-system/x-window-and-vnc.html)
-for running the GUI.
-
- $ module load intel
- $ idb
-
-Read more at the [Intel
-Debugger](intel-suite/intel-debugger.html) page.
-
-Allinea Forge (DDT/MAP)
------------------------
-
-Allinea DDT, is a commercial debugger primarily for debugging parallel
-MPI or OpenMP programs. It also has a support for GPU (CUDA) and Intel
-Xeon Phi accelerators. DDT provides all the standard debugging features
-(stack trace, breakpoints, watches, view variables, threads etc.) for
-every thread running as part of your program, or for every process -
-even if these processes are distributed across a cluster using an MPI
-implementation.
-
- $ module load Forge
- $ forge
-
-Read more at the [Allinea
-DDT](debuggers/allinea-ddt.html) page.
-
-Allinea Performance Reports
----------------------------
-
-Allinea Performance Reports characterize the performance of HPC
-application runs. After executing your application through the tool, a
-synthetic HTML report is generated automatically, containing information
-about several metrics along with clear behavior statements and hints to
-help you improve the efficiency of your runs. Our license is limited to
-64 MPI processes.
-
- $ module load PerformanceReports/6.0
- $ perf-report mpirun -n 64 ./my_application argument01 argument02
-
-Read more at the [Allinea Performance
-Reports](debuggers/allinea-performance-reports.html)
-page.
-
-RougeWave Totalview
--------------------
-
-TotalView is a source- and machine-level debugger for multi-process,
-multi-threaded programs. Its wide range of tools provides ways to
-analyze, organize, and test programs, making it easy to isolate and
-identify problems in individual threads and processes in programs of
-great complexity.
-
- $ module load TotalView/8.15.4-6-linux-x86-64
- $ totalview
-
-Read more at the [Totalview](debuggers/total-view.html)
-page.
-
-Vampir trace analyzer
----------------------
-
-Vampir is a GUI trace analyzer for traces in OTF format.
-
- $ module load Vampir/8.5.0
- $ vampir
-
-Read more at the [Vampir](debuggers/vampir.html) page.
-
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-Aislinn
-=======
-
-- Aislinn is a dynamic verifier for MPI programs. For a fixed input it
- covers all possible runs with respect to nondeterminism introduced
- by MPI. It allows to detect bugs (for sure) that occurs very rare in
- normal runs.
-- Aislinn detects problems like invalid memory accesses, deadlocks,
- misuse of MPI, and resource leaks.
-- Aislinn is open-source software; you can use it without any
- licensing limitations.
-- Web page of the project: <http://verif.cs.vsb.cz/aislinn/>
-
-Note
-
-Aislinn is software developed at IT4Innovations and some parts are still
-considered experimental. If you have any questions or experienced any
-problems, please contact the author: <stanislav.bohm@vsb.cz>.
-
-### Usage
-
-Let us have the following program that contains a bug that is not
-manifested in all runs:
-
-`
-#include <mpi.h>
-#include <stdlib.h>
-
-int main(int argc, char **argv) {
- int rank;
-
- MPI_Init(&argc, &argv);
- MPI_Comm_rank(MPI_COMM_WORLD, &rank);
-
- if (rank == 0) {
- int *mem1 = (int*) malloc(sizeof(int) * 2);
- int *mem2 = (int*) malloc(sizeof(int) * 3);
- int data;
- MPI_Recv(&data, 1, MPI_INT, MPI_ANY_SOURCE, 1,
- MPI_COMM_WORLD, MPI_STATUS_IGNORE);
- mem1[data] = 10; // <---------- Possible invalid memory write
- MPI_Recv(&data, 1, MPI_INT, MPI_ANY_SOURCE, 1,
- MPI_COMM_WORLD, MPI_STATUS_IGNORE);
- mem2[data] = 10;
- free(mem1);
- free(mem2);
- }
-
- if (rank == 1 || rank == 2) {
- MPI_Send(&rank, 1, MPI_INT, 0, 1, MPI_COMM_WORLD);
- }
-
- MPI_Finalize();
- return 0;
-}
-`
-
-The program does the following: process 0 receives two messages from
-anyone and processes 1 and 2 send a message to process 0. If a message
-from process 1 is received first, then the run does not expose the
-error. If a message from process 2 is received first, then invalid
-memory write occurs at line 16.
-
-To verify this program by Aislinn, we first load Aislinn itself:
-
-`
-$ module load aislinn
-`
-
-Now we compile the program by Aislinn implementation of MPI. There are
-`mpicc` for C programs and `mpicxx`{.docutils
-.literal} for C++ programs. Only MPI parts of the verified application
-has to be recompiled; non-MPI parts may remain untouched. Let us assume
-that our program is in `test.cpp`.
-
-`
-$ mpicc -g test.cpp -o test
-`
-
-The `-g` flag is not necessary, but it puts more
-debugging information into the program, hence Aislinn may provide more
-detailed report. The command produces executable file `test`{.docutils
-.literal}.
-
-Now we run the Aislinn itself. The argument `-p 3`
-specifies that we want to verify our program for the case of three MPI
-processes
-
-`
-$ aislinn -p 3 ./test
-==AN== INFO: Aislinn v0.3.0
-==AN== INFO: Found error 'Invalid write'
-==AN== INFO: 1 error(s) found
-==AN== INFO: Report written into 'report.html'
-`
-
-Aislinn found an error and produced HTML report. To view it, we can use
-any browser, e.g.:
-
- $ firefox report.html
-
-At the beginning of the report there are some basic summaries of the
-verification. In the second part (depicted in the following picture),
-the error is described.
-
-
-It shows us:
-
- - Error occurs in process 0 in test.cpp on line 16.
- - Stdout and stderr streams are empty. (The program does not
- write anything).
- - The last part shows MPI calls for each process that occurs in the
- invalid run. The more detailed information about each call can be
- obtained by mouse cursor.
-
-### Limitations
-
-Since the verification is a non-trivial process there are some of
-limitations.
-
-- The verified process has to terminate in all runs, i.e. we cannot
- answer the halting problem.
-- The verification is a computationally and memory demanding process.
- We put an effort to make it efficient and it is an important point
- for further research. However covering all runs will be always more
- demanding than techniques that examines only a single run. The good
- practise is to start with small instances and when it is feasible,
- make them bigger. The Aislinn is good to find bugs that are hard to
- find because they occur very rarely (only in a rare scheduling).
- Such bugs often do not need big instances.
-- Aislinn expects that your program is a "standard MPI" program, i.e.
- processes communicate only through MPI, the verified program does
- not interacts with the system in some unusual ways (e.g.
- opening sockets).
-
-There are also some limitations bounded to the current version and they
-will be removed in the future:
-
-- All files containing MPI calls have to be recompiled by MPI
- implementation provided by Aislinn. The files that does not contain
- MPI calls, they do not have to recompiled. Aislinn MPI
- implementation supports many commonly used calls from MPI-2 and
- MPI-3 related to point-to-point communication, collective
- communication, and communicator management. Unfortunately, MPI-IO
- and one-side communication is not implemented yet.
-- Each MPI can use only one thread (if you use OpenMP, set
- `OMP_NUM_THREADS` to 1).
-- There are some limitations for using files, but if the program just
- reads inputs and writes results, it is ok.
-
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-Allinea Forge (DDT,MAP)
-=======================
-
-
-
-Allinea Forge consist of two tools - debugger DDT and profiler MAP.
-
-Allinea DDT, is a commercial debugger primarily for debugging parallel
-MPI or OpenMP programs. It also has a support for GPU (CUDA) and Intel
-Xeon Phi accelerators. DDT provides all the standard debugging features
-(stack trace, breakpoints, watches, view variables, threads etc.) for
-every thread running as part of your program, or for every process -
-even if these processes are distributed across a cluster using an MPI
-implementation.
-
-Allinea MAP is a profiler for C/C++/Fortran HPC codes. It is designed
-for profiling parallel code, which uses pthreads, OpenMP or MPI.
-
-License and Limitations for the clusters Users
-----------------------------------------------
-
-On the clusters users can debug OpenMP or MPI code that runs up to 64
-parallel processes. In case of debugging GPU or Xeon Phi accelerated
-codes the limit is 8 accelerators. These limitation means that:
-
-- 1 user can debug up 64 processes, or
-- 32 users can debug 2 processes, etc.
-
-In case of debugging on accelerators:
-
-- 1 user can debug on up to 8 accelerators, orÂ
-- 8 users can debug on single accelerator.Â
-
-Compiling Code to run with Forge
---------------------------------
-
-### Modules
-
-Load all necessary modules to compile the code. For example:Â
-
- $ module load intel
- $ module load impi ... or ... module load OpenMPI
-
-Load the Allinea DDT module:
-
- $ module load Forge
-
-Compile the code:
-
-`
-$ mpicc -g -O0 -o test_debug test.c
-
-$ mpif90 -g -O0 -o test_debug test.f
-`
-
-### Compiler flags
-
-Before debugging, you need to compile your code with theses flags:
-
--g** : Generates extra debugging information usable by GDB. -g3**
-includes even more debugging information. This option is available for
-GNU and INTEL C/C++ and Fortran compilers.
-
--O0** : Suppress all optimizations.**
-
-Â
-
-Direct starting a Job with Forge
---------------------------------
-
-Be sure to log in with an [ X window
-forwarding
-enabled](../../../get-started-with-it4innovations/accessing-the-clusters/graphical-user-interface/x-window-system/x-window-and-vnc.html).
-This could mean using the -X in the ssh: Â
-
- $ ssh -X username@clustername.it4i.cz
-
-Other options is to access login node using VNC. Please see the detailed
-information on [how to
-use graphic user interface on the
-clusters](../../../get-started-with-it4innovations/accessing-the-clusters/graphical-user-interface/x-window-system/x-window-and-vnc.html)
-.
-
-From the login node an interactive session **with X windows forwarding**
-(-X option) can be started by following command:Â
-
- $ qsub -I -X -A NONE-0-0 -q qexp -lselect=1:ncpus=24:mpiprocs=24,walltime=01:00:00Â
-
-Then launch the debugger with the ddt command followed by the name of
-the executable to debug:
-
- $ ddt test_debug
-
-Forge now has common GUI for both DDT and MAP. In interactive mode, you
-can launch Forge using forge, ddt or map,
-the latter two will just launch forge and swith to the respective
-tab in the common GUI.
-
-A submission window that appears have
-a prefilled path to the executable to debug. You can select the number
-of MPI processors and/or OpenMP threads on which to run and press run.
-Command line arguments to a program can be entered to the
-"Arguments "
-box.
-
-
-Â Â
-
-To start the debugging directly without the submission window, user can
-specify the debugging and execution parameters from the command line.
-For example the number of MPI processes is set by option "-np 4".
-Skipping the dialog is done by "-start" option. To see the list of the
-"ddt" command line parameters, run "ddt --help". Â
-
- ddt -start -np 4 ./hello_debug_impi
-
-All of the above text also applies for MAP, just replace ddt command
-with map.
-
-Reverse connect
----------------
-
-Forge now provides a new convenient mode of operation, called Reverse
-connect. Instead of launching a job from the GUI, the process is
-reserved - DDT/MAP is launched as a server in the job which then
-connects to a running instance of your GUI.
-
-To use Reverse connect, use a jobscript that you would normally use to
-launch your application, just prepend ddt/map --connect to your
-application:
-
- map --connect mpirun -np 24 ./mpi-test
- ddt --connect mpirun -np 24 ./mpi-test
-
-Launch Forge GUI on login node and submit the job using qsub. When the
-job starts running, Forge will ask you to accept the connection:
-
-
-
-After accepting the request, you can start remote profiling/debugging.
-
-Xeon Phi
---------
-
-Forge allows debugging and profiling of both offload and native mode
-Xeon Phi programs.
-
-### Offload mode
-
-It is recommended to set the following environment values on the offload
-host:
-
- export MYO_WATCHDOG_MONITOR=-1 # To make sure the host process isn't killed when we enter a debugging session
- export AMPLXE_COI_DEBUG_SUPPORT=true # To make sure that debugging symbols are accessible on the host and the card
- unset OFFLOAD_MAIN # To make sure allinea DDT can attach to offloaded codes
-
-Then use one of the above mentioned methods to launch Forge. (Reverse
-connect also works.)
-
-### Native mode
-
-Native mode programs can be profiled/debugged using the remote launch
-feature. First, you need to create a script that will setup the
-environment on the Phi card. An example:
-
- #!/bin/bash
- # adjust PATH and LD_LIBRARY_PATH according to the toolchain/libraries your app is using.
- export PATH=/apps/all/impi/5.0.3.048-iccifort-2015.3.187/mic/bin:$PATH
- export LD_LIBRARY_PATH=/apps/all/impi/5.0.3.048-iccifort-2015.3.187/mic/lib:/apps/all/ifort/2015.3.187/lib/mic:/apps/all/icc/2015.3.187/lib/mic:$LD_LIBRARY_PATH
- export MIC_OMP_NUM_THREADS=60
- export MYO_WATCHDOG_MONITOR=-1
- export AMPLXE_COI_DEBUG_SUPPORT=true
- unset OFFLOAD_MAIN
- export I_MPI_MIC=1
-
-Save the script in eg. ~/remote-mic.sh.
-Now, start an interactive graphical session on a node with
-accelerator:
-
- $ qsub â€IX â€q qexp â€l select=1:ncpus=24:accelerator=True
-
-Launch Forge :
-
- $ module load Forge
- $ forge&
-
-Now click on the remote launch drop-down list, select "Configure..." and
-Add a new remote connection with the following parameters:
-
-Connection name: mic0
-
-Hostname: mic0
-
-Remote Installation Directory:Â /apps/all/Forge/5.1-43967/
-
-Remote script: ~/remote-mic.sh
-
-You can click Test Remote Launch to verify the configuration. After you
-save the remote launch configuration and select it in the dropdown list,
-you can use the Run button in the main windows to remotely launch your
-program on mic0.
-
-Documentation
--------------
-
-Users can find original User Guide after loading the Forge module:Â
-
- $EBROOTFORGE/doc/userguide-forge.pdf
-
-Â
-
-Â
-
-Â [1] Discipline, Magic, Inspiration and Science: Best Practice
-Debugging with Allinea DDT, Workshop conducted at LLNL by Allinea on May
-10, 2013,
-[link](https://computing.llnl.gov/tutorials/allineaDDT/index.html)
-
-Â
-
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-Allinea Performance Reports
-===========================
-
-quick application profiling
-
-
-
-Introduction
-------------
-
-Allinea Performance Reports characterize the performance of HPC
-application runs. After executing your application through the tool, a
-synthetic HTML report is generated automatically, containing information
-about several metrics along with clear behavior statements and hints to
-help you improve the efficiency of your runs.
-
-The Allinea Performance Reports is most useful in profiling MPI
-programs.
-
-Our license is limited to 64 MPI processes.
-
-Modules
--------
-
-Allinea Performance Reports version 6.0 is available
-
- $ module load PerformanceReports/6.0
-
-The module sets up environment variables, required for using the Allinea
-Performance Reports.Â
-
-Usage
------
-
-Use the the perf-report wrapper on your (MPI) program.
-
-Instead of [running your MPI program the usual
-way](../mpi-1.html), use the the perf report wrapper:
-
- $ perf-report mpirun ./mympiprog.x
-
-The mpi program will run as usual. The perf-report creates two
-additional files, in *.txt and *.html format, containing the
-performance report. Note that demanding MPI
-codes should be run within [ the queue
-system](../../resource-allocation-and-job-execution/job-submission-and-execution.html).
-
-Example
--------
-
-In this example, we will be profiling the mympiprog.x MPI program, using
-Allinea performance reports. Assume that the code is compiled with intel
-compilers and linked against intel MPI library:
-
-First, we allocate some nodes via the express queue:
-
- $ qsub -q qexp -l select=2:ppn=24:mpiprocs=24:ompthreads=1 -I
- qsub: waiting for job 262197.dm2 to start
- qsub: job 262197.dm2 ready
-
-Then we load the modules and run the program the usual way:
-
- $ module load intel impi PerfReports/6.0
- $ mpirun ./mympiprog.x
-
-Now lets profile the code:
-
- $ perf-report mpirun ./mympiprog.x
-
-Performance report files
-[mympiprog_32p*.txt](mympiprog_32p_2014-10-15_16-56.txt)
-and
-[mympiprog_32p*.html](mympiprog_32p_2014-10-15_16-56.html)
-were created. We can see that the code is very efficient on MPI and is
-CPU bounded.
-
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-Intel VTune Amplifier XE
-========================
-
-
-
-Introduction
-------------
-
-Intel*® *VTune™ >Amplifier, part of Intel Parallel studio, is a GUI
-profiling tool designed for Intel processors. It offers a graphical
-performance analysis of single core and multithreaded applications. A
-highlight of the features:
-
-- Hotspot analysis
-- Locks and waits analysis
-- Low level specific counters, such as branch analysis and memory
- bandwidth
-- Power usage analysis - frequency and sleep states.
-
-
-
-Usage
------
-
-To profile an application with VTune Amplifier, special kernel
-modules need to be loaded. The modules are not loaded on the login
-nodes, thus direct profiling on login nodes is not possible. By default,
-the kernel modules ale not loaded on compute nodes neither. In order to
-have the modules loaded, you need to specify vtune=version PBS resource
-at job submit. The version is the same as for *environment module*. For
-example to use
-VTune/2016_update1:
-
- $ qsub -q qexp -A OPEN-0-0 -I -l select=1,vtune=2016_update1
-
-After that, you can verify the modules sep*, pax and vtsspp are present
-in the kernel :
-
- $ lsmod | grep -e sep -e pax -e vtsspp
- vtsspp 362000 0
- sep3_15 546657 0
- pax 4312 0
-
-To launch the GUI, first load the module:
-
- $ module add VTune/2016_update1
-
- class="s1">and launch the GUI :
-
- $Â amplxe-gui
-
-The GUI will open in new window. Click on "*New Project...*" to
-create a new project. After clicking *OK*, a new window with project
-properties will appear. Â At "*Application:*", select the bath to your
-binary you want to profile (the binary should be compiled with -g flag).
-Some additional options such as command line arguments can be selected.
-At "*Managed code profiling mode:*" select "*Native*" (unless you want
-to profile managed mode .NET/Mono applications). After clicking *OK*,
-your project is created.
-
-To run a new analysis, click "*New analysis...*". You will see a list of
-possible analysis. Some of them will not be possible on the current CPU
-(eg. Intel Atom analysis is not possible on Sandy bridge CPU), the GUI
-will show an error box if you select the wrong analysis. For example,
-select "*Advanced Hotspots*". Clicking on *Start *will start profiling
-of the application.
-
-Remote Analysis
----------------
-
-VTune Amplifier also allows a form of remote analysis. In this mode,
-data for analysis is collected from the command line without GUI, and
-the results are then loaded to GUI on another machine. This allows
-profiling without interactive graphical jobs. To perform a remote
-analysis, launch a GUI somewhere, open the new analysis window and then
-click the button "*Command line*" in bottom right corner. It will show
-the command line needed to perform the selected analysis.
-
-The command line will look like this:
-
- /apps/all/VTune/2016_update1/vtune_amplifier_xe_2016.1.1.434111/bin64/amplxe-cl -collect advanced-hotspots -app-working-dir /home/sta545/tmp -- /home/sta545/tmp/sgemm
-
-Copy the line to clipboard and then you can paste it in your jobscript
-or in command line. After the collection is run, open the GUI once
-again, click the menu button in the upper right corner, and select
-"*Open > Result...*". The GUI will load the results from the run.
-
-Xeon Phi
---------
-
-It is possible to analyze both native and offloaded Xeon Phi
-applications.Â
-
-### Native mode
-
-This mode is useful for native Xeon Phi applications launched directly
-on the card. In *Analysis Target* window, select *Intel Xeon Phi
-coprocessor (native), *choose path to the binary and MIC card to run on.
-
-### Offload mode
-
-This mode is useful for applications that are launched from the host and
-use offload, OpenCL or mpirun. In *Analysis Target* window,
-select *Intel Xeon Phi coprocessor (native), *choose path to the binary
-and MIC card to run on.
-
-Â
-
-If the analysis is interrupted or aborted, further analysis on the card
-might be impossible and you will get errors like "ERROR connecting to
-MIC card". In this case please contact our support to reboot the MIC
-card.
-
-You may also use remote analysis to collect data from the MIC and then
-analyze it in the GUI later :
-
-Native launch:
-
- $ /apps/all/VTune/2016_update1/vtune_amplifier_xe_2016.1.1.434111/bin64/amplxe-cl -target-system mic-native:0 -collect advanced-hotspots -- /home/sta545/tmp/vect-add-mic
-
-Host launch:
-
- $ /apps/all/VTune/2016_update1/vtune_amplifier_xe_2016.1.1.434111/bin64/amplxe-cl -target-system mic-host-launch:0 -collect advanced-hotspots -- /home/sta545/tmp/sgemm
-
-You can obtain this command line by pressing the "Command line..."
-button on Analysis Type screen.Â
-
-References
-----------
-
-1. ><https://www.rcac.purdue.edu/tutorials/phi/PerformanceTuningXeonPhi-Tullos.pdf>Â Performance
- Tuning for Intel® Xeon Phi™ Coprocessors
-2. ><https://software.intel.com/en-us/intel-vtune-amplifier-xe-support/documentation> >Intel®
- VTune™ Amplifier Support
-3. ><https://software.intel.com/en-us/amplifier_help_linux>Â Linux
- user guide
-
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-Debuggers and profilers summary
-===============================
-
-
-
-Introduction
-------------
-
-We provide state of the art programms and tools to develop, profile and
-debug HPC codes at IT4Innovations.
-On these pages, we provide an overview of the profiling and debugging
-tools available on Anslem at IT4I.
-
-Intel debugger
---------------
-
-Intel debugger is no longer available since Parallel Studio version 2015
-
-The intel debugger version 13.0 is available, via module intel. The
-debugger works for applications compiled with C and C++ compiler and the
-ifort fortran 77/90/95 compiler. The debugger provides java GUI
-environment. Use [X
-display](../../../get-started-with-it4innovations/accessing-the-clusters/graphical-user-interface/x-window-system/x-window-and-vnc.html)
-for running the GUI.
-
- $ module load intel
- $ idb
-
-Read more at the [Intel
-Debugger](../intel-suite/intel-debugger.html) page.
-
-Allinea Forge (DDT/MAP)
------------------------
-
-Allinea DDT, is a commercial debugger primarily for debugging parallel
-MPI or OpenMP programs. It also has a support for GPU (CUDA) and Intel
-Xeon Phi accelerators. DDT provides all the standard debugging features
-(stack trace, breakpoints, watches, view variables, threads etc.) for
-every thread running as part of your program, or for every process -
-even if these processes are distributed across a cluster using an MPI
-implementation.
-
- $ module load Forge
- $ forge
-
-Read more at the [Allinea DDT](allinea-ddt.html) page.
-
-Allinea Performance Reports
----------------------------
-
-Allinea Performance Reports characterize the performance of HPC
-application runs. After executing your application through the tool, a
-synthetic HTML report is generated automatically, containing information
-about several metrics along with clear behavior statements and hints to
-help you improve the efficiency of your runs. Our license is limited to
-64 MPI processes.
-
- $ module load PerformanceReports/6.0
- $ perf-report mpirun -n 64 ./my_application argument01 argument02
-
-Read more at the [Allinea Performance
-Reports](allinea-performance-reports.html) page.
-
-RougeWave Totalview
--------------------
-
-TotalView is a source- and machine-level debugger for multi-process,
-multi-threaded programs. Its wide range of tools provides ways to
-analyze, organize, and test programs, making it easy to isolate and
-identify problems in individual threads and processes in programs of
-great complexity.
-
- $ module load TotalView/8.15.4-6-linux-x86-64
- $ totalview
-
-Read more at the [Totalview](total-view.html) page.
-
-Vampir trace analyzer
----------------------
-
-Vampir is a GUI trace analyzer for traces in OTF format.
-
- $ module load Vampir/8.5.0
- $ vampir
-
-Read more at the [Vampir](vampir.html) page.
-
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-Total View
-==========
-
-TotalView is a GUI-based source code multi-process, multi-thread
-debugger.
-
-License and Limitations for cluster Users
------------------------------------------
-
-On the cluster users can debug OpenMP or MPI code that runs up to 64
-parallel processes. These limitation means that:
-
-Â Â Â 1 user can debug up 64 processes, or
-Â Â Â 32 users can debug 2 processes, etc.
-
-Debugging of GPU accelerated codes is also supported.
-
-You can check the status of the licenses
-[here](https://extranet.it4i.cz/rsweb/anselm/license/totalview).
-
-Compiling Code to run with TotalView
-------------------------------------
-
-### Modules
-
-Load all necessary modules to compile the code. For example:
-
- module load intel
-
- module load impi  ... or ... module load OpenMPI/X.X.X-icc
-
-Load the TotalView module:
-
- module load TotalView/8.15.4-6-linux-x86-64
-
-Compile the code:
-
- mpicc -g -O0 -o test_debug test.c
-
- mpif90 -g -O0 -o test_debug test.f
-
-### Compiler flags
-
-Before debugging, you need to compile your code with theses flags:
-
--g** : Generates extra debugging information usable by GDB. -g3**
-includes even more debugging information. This option is available for
-GNU and INTEL C/C++ and Fortran compilers.
-
--O0** : Suppress all optimizations.**
-
-Starting a Job with TotalView
------------------------------
-
-Be sure to log in with an X window forwarding enabled. This could mean
-using the -X in the ssh:Â
-
- ssh -X username@salomon.it4i.cz
-
-Other options is to access login node using VNC. Please see the detailed
-information on how to use graphic user interface on Anselm
-[here](https://docs.it4i.cz/salomon/software/debuggers/resolveuid/11e53ad0d2fd4c5187537f4baeedff33#VNC).
-
-From the login node an interactive session with X windows forwarding (-X
-option) can be started by following command:
-
- qsub -I -X -A NONE-0-0 -q qexp -lselect=1:ncpus=24:mpiprocs=24,walltime=01:00:00
-
-Then launch the debugger with the totalview command followed by the name
-of the executable to debug.
-
-### Debugging a serial code
-
-To debug a serial code use:
-
- totalview test_debug
-
-### Debugging a parallel code - option 1
-
-To debug a parallel code compiled with >**OpenMPI** you need
-to setup your TotalView environment:Â
-
-Please note:** To be able to run parallel debugging procedure from the
-command line without stopping the debugger in the mpiexec source code
-you have to add the following function to your **~/.tvdrc** file:
-
- proc mpi_auto_run_starter {loaded_id} {
- Â Â Â set starter_programs {mpirun mpiexec orterun}
- Â Â Â set executable_name [TV::symbol get $loaded_id full_pathname]
- Â Â Â set file_component [file tail $executable_name]
-
- Â Â Â if {[lsearch -exact $starter_programs $file_component] != -1} {
- Â Â Â Â Â Â Â puts "*************************************"
- Â Â Â Â Â Â Â puts "Automatically starting $file_component"
- Â Â Â Â Â Â Â puts "*************************************"
- Â Â Â Â Â Â Â dgo
- Â Â Â }
- }
-
- # Append this function to TotalView's image load callbacks so that
- # TotalView run this program automatically.
-
- dlappend TV::image_load_callbacks mpi_auto_run_starter
-
-The source code of this function can be also found in
-
- /apps/all/OpenMPI/1.10.1-GNU-4.9.3-2.25/etc/openmpi-totalview.tcl
-
-You can also add only following line to you ~/.tvdrc file instead of
-the entire function:
-
-source /apps/all/OpenMPI/1.10.1-GNU-4.9.3-2.25/etc/openmpi-totalview.tcl**
-
-You need to do this step only once. See also [OpenMPI FAQ
-entry](https://www.open-mpi.org/faq/?category=running#run-with-tv)
-
-Now you can run the parallel debugger using:
-
- mpirun -tv -n 5 ./test_debug
-
-When following dialog appears click on "Yes"
-
-
-
-At this point the main TotalView GUI window will appear and you can
-insert the breakpoints and start debugging:
-
-
-
-### Debugging a parallel code - option 2
-
-Other option to start new parallel debugging session from a command line
-is to let TotalView to execute mpirun by itself. In this case user has
-to specify a MPI implementation used to compile the source code.Â
-
-The following example shows how to start debugging session with Intel
-MPI:
-
- module load intel/2015b-intel-2015b impi/5.0.3.048-iccifort-2015.3.187-GNU-5.1.0-2.25 TotalView/8.15.4-6-linux-x86-64
-
- totalview -mpi "Intel MPI-Hydra" -np 8 ./hello_debug_impi
-
-After running previous command you will see the same window as shown in
-the screenshot above.
-
-More information regarding the command line parameters of the TotalView
-can be found TotalView Reference Guide, Chapter 7: TotalView Command
-Syntax. Â
-
-Documentation
--------------
-
-[1] The [TotalView
-documentation](http://www.roguewave.com/support/product-documentation/totalview-family.aspx#totalview)
-web page is a good resource for learning more about some of the advanced
-TotalView features.
-
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-Valgrind
-========
-
-Valgrind is a tool for memory debugging and profiling.
-
-About Valgrind
---------------
-
-Valgrind is an open-source tool, used mainly for debuggig memory-related
-problems, such as memory leaks, use of uninitalized memory etc. in C/C++
-applications. The toolchain was however extended over time with more
-functionality, such as debugging of threaded applications, cache
-profiling, not limited only to C/C++.
-
-Valgind is an extremely useful tool for debugging memory errors such as
-[off-by-one](http://en.wikipedia.org/wiki/Off-by-one_error).
-Valgrind uses a virtual machine and dynamic recompilation of binary
-code, because of that, you can expect that programs being debugged by
-Valgrind run 5-100 times slower.
-
-The main tools available in Valgrind are :
-
-- **Memcheck**, the original, must used and default tool. Verifies
- memory access in you program and can detect use of unitialized
- memory, out of bounds memory access, memory leaks, double free, etc.
-- **Massif**, a heap profiler.
-- **Hellgrind** and **DRD** can detect race conditions in
- multi-threaded applications.
-- **Cachegrind**, a cache profiler.
-- **Callgrind**, a callgraph analyzer.
-- For a full list and detailed documentation, please refer to the
- [official Valgrind
- documentation](http://valgrind.org/docs/).
-
-Installed versions
-------------------
-
-There are two versions of Valgrind available on the cluster.
-
-- >Version 3.8.1, installed by operating system vendor
- in /usr/bin/valgrind.
- >This version is available by default, without the need
- to load any module. This version however does not provide additional
- MPI support. Also, it does not support AVX2 instructions,
- **debugging of an AVX2-enabled executable with this version will
- fail**
-- >Version 3.11.0 built by ICC with support for Intel MPI,
- available in
- [module](../../environment-and-modules.html)Â
- Valgrind/3.11.0-intel-2015b. After loading
- the module, this version replaces the default valgrind.
-- Version 3.11.0 built by GCC with support for Open MPI, module
- Valgrind/3.11.0-foss-2015b
-
-Usage
------
-
-Compile the application which you want to debug as usual. It is
-advisable to add compilation flags -g (to
-add debugging information to the binary so that you will see original
-source code lines in the output) and -O0
-(to disable compiler optimizations).Â
-
-For example, lets look at this C code, which has two problems :
-
- #include <stdlib.h>
-
- void f(void)
- {
- int* x = malloc(10 * sizeof(int));
- x[10] = 0; // problem 1: heap block overrun
- } // problem 2: memory leak -- x not freed
-
- int main(void)
- {
- f();
- return 0;
- }
-
-Now, compile it with Intel compiler :
-
- $ module add intel
- $ icc -g valgrind-example.c -o valgrind-exampleÂ
-
-Now, lets run it with Valgrind. The syntax is :
-
- valgrind [valgrind options] <your program
-binary> [your program options]
-
-If no Valgrind options are specified, Valgrind defaults to running
-Memcheck tool. Please refer to the Valgrind documentation for a full
-description of command line options.
-
- $ valgrind ./valgrind-example
- ==12652== Memcheck, a memory error detector
- ==12652== Copyright (C) 2002-2013, and GNU GPL'd, by Julian Seward et al.
- ==12652== Using Valgrind-3.9.0 and LibVEX; rerun with -h for copyright info
- ==12652== Command: ./valgrind-example
- ==12652==
- ==12652== Invalid write of size 4
- ==12652== at 0x40053E: f (valgrind-example.c:6)
- ==12652== by 0x40054E: main (valgrind-example.c:11)
- ==12652== Address 0x5861068 is 0 bytes after a block of size 40 alloc'd
- ==12652== at 0x4C27AAA: malloc (vg_replace_malloc.c:291)
- ==12652== by 0x400528: f (valgrind-example.c:5)
- ==12652== by 0x40054E: main (valgrind-example.c:11)
- ==12652==
- ==12652==
- ==12652== HEAP SUMMARY:
- ==12652== in use at exit: 40 bytes in 1 blocks
- ==12652== total heap usage: 1 allocs, 0 frees, 40 bytes allocated
- ==12652==
- ==12652== LEAK SUMMARY:
- ==12652== definitely lost: 40 bytes in 1 blocks
- ==12652== indirectly lost: 0 bytes in 0 blocks
- ==12652== possibly lost: 0 bytes in 0 blocks
- ==12652== still reachable: 0 bytes in 0 blocks
- ==12652== suppressed: 0 bytes in 0 blocks
- ==12652== Rerun with --leak-check=full to see details of leaked memory
- ==12652==
- ==12652== For counts of detected and suppressed errors, rerun with: -v
- ==12652== ERROR SUMMARY: 1 errors from 1 contexts (suppressed: 6 from 6)
-
-In the output we can see that Valgrind has detected both errors - the
-off-by-one memory access at line 5 and a memory leak of 40 bytes. If we
-want a detailed analysis of the memory leak, we need to run Valgrind
-with --leak-check=full option :
-
- $ valgrind --leak-check=full ./valgrind-example
- ==23856== Memcheck, a memory error detector
- ==23856== Copyright (C) 2002-2010, and GNU GPL'd, by Julian Seward et al.
- ==23856== Using Valgrind-3.6.0 and LibVEX; rerun with -h for copyright info
- ==23856== Command: ./valgrind-example
- ==23856==
- ==23856== Invalid write of size 4
- ==23856== at 0x40067E: f (valgrind-example.c:6)
- ==23856== by 0x40068E: main (valgrind-example.c:11)
- ==23856== Address 0x66e7068 is 0 bytes after a block of size 40 alloc'd
- ==23856== at 0x4C26FDE: malloc (vg_replace_malloc.c:236)
- ==23856== by 0x400668: f (valgrind-example.c:5)
- ==23856== by 0x40068E: main (valgrind-example.c:11)
- ==23856==
- ==23856==
- ==23856== HEAP SUMMARY:
- ==23856== in use at exit: 40 bytes in 1 blocks
- ==23856== total heap usage: 1 allocs, 0 frees, 40 bytes allocated
- ==23856==
- ==23856== 40 bytes in 1 blocks are definitely lost in loss record 1 of 1
- ==23856== at 0x4C26FDE: malloc (vg_replace_malloc.c:236)
- ==23856== by 0x400668: f (valgrind-example.c:5)
- ==23856== by 0x40068E: main (valgrind-example.c:11)
- ==23856==
- ==23856== LEAK SUMMARY:
- ==23856== definitely lost: 40 bytes in 1 blocks
- ==23856== indirectly lost: 0 bytes in 0 blocks
- ==23856== possibly lost: 0 bytes in 0 blocks
- ==23856== still reachable: 0 bytes in 0 blocks
- ==23856== suppressed: 0 bytes in 0 blocks
- ==23856==
- ==23856== For counts of detected and suppressed errors, rerun with: -v
- ==23856== ERROR SUMMARY: 2 errors from 2 contexts (suppressed: 6 from 6)
-
-Now we can see that the memory leak is due to the
-malloc() at line 6.
-
-Usage with MPI
----------------------------
-
-Although Valgrind is not primarily a parallel debugger, it can be used
-to debug parallel applications as well. When launching your parallel
-applications, prepend the valgrind command. For example :
-
- $ mpirun -np 4 valgrind myapplication
-
-The default version without MPI support will however report a large
-number of false errors in the MPI library, such as :
-
- ==30166== Conditional jump or move depends on uninitialised value(s)
- ==30166== at 0x4C287E8: strlen (mc_replace_strmem.c:282)
- ==30166== by 0x55443BD: I_MPI_Processor_model_number (init_interface.c:427)
- ==30166== by 0x55439E0: I_MPI_Processor_arch_code (init_interface.c:171)
- ==30166== by 0x558D5AE: MPID_nem_impi_init_shm_configuration (mpid_nem_impi_extensions.c:1091)
- ==30166== by 0x5598F4C: MPID_nem_init_ckpt (mpid_nem_init.c:566)
- ==30166== by 0x5598B65: MPID_nem_init (mpid_nem_init.c:489)
- ==30166== by 0x539BD75: MPIDI_CH3_Init (ch3_init.c:64)
- ==30166== by 0x5578743: MPID_Init (mpid_init.c:193)
- ==30166== by 0x554650A: MPIR_Init_thread (initthread.c:539)
- ==30166== by 0x553369F: PMPI_Init (init.c:195)
- ==30166== by 0x4008BD: main (valgrind-example-mpi.c:18)
-
-so it is better to use the MPI-enabled valgrind from module. The MPI
-versions requires library :Â
-
-$EBROOTVALGRIND/lib/valgrind/libmpiwrap-amd64-linux.so
-
-which must be included in the LD_PRELOAD
-environment variable.
-
-Lets look at this MPI example :
-
- #include <stdlib.h>
- #include <mpi.h>Â
-
- int main(int argc, char *argv[])
- {
- Â Â Â Â Â int *data = malloc(sizeof(int)*99);
-
- Â Â Â Â Â MPI_Init(&argc, &argv);
- Â Â Â Â MPI_Bcast(data, 100, MPI_INT, 0, MPI_COMM_WORLD);
- Â Â Â Â Â MPI_Finalize();Â
-
- Â Â Â Â Â Â Â return 0;
- }
-
-There are two errors - use of uninitialized memory and invalid length of
-the buffer. Lets debug it with valgrind :
-
- $ module add intel impi
- $ mpiicc -g valgrind-example-mpi.c -o valgrind-example-mpi
- $ module add Valgrind/3.11.0-intel-2015b
- $ mpirun -np 2 -env LD_PRELOAD $EBROOTVALGRIND/lib/valgrind/libmpiwrap-amd64-linux.so valgrind ./valgrind-example-mpi
-
-Prints this output : (note that there is output printed for every
-launched MPI process)
-
- ==31318== Memcheck, a memory error detector
- ==31318== Copyright (C) 2002-2013, and GNU GPL'd, by Julian Seward et al.
- ==31318== Using Valgrind-3.9.0 and LibVEX; rerun with -h for copyright info
- ==31318== Command: ./valgrind-example-mpi
- ==31318==
- ==31319== Memcheck, a memory error detector
- ==31319== Copyright (C) 2002-2013, and GNU GPL'd, by Julian Seward et al.
- ==31319== Using Valgrind-3.9.0 and LibVEX; rerun with -h for copyright info
- ==31319== Command: ./valgrind-example-mpi
- ==31319==
- valgrind MPI wrappers 31319: Active for pid 31319
- valgrind MPI wrappers 31319: Try MPIWRAP_DEBUG=help for possible options
- valgrind MPI wrappers 31318: Active for pid 31318
- valgrind MPI wrappers 31318: Try MPIWRAP_DEBUG=help for possible options
- ==31319== Unaddressable byte(s) found during client check request
- ==31319== at 0x4E35974: check_mem_is_addressable_untyped (libmpiwrap.c:960)
- ==31319== by 0x4E5D0FE: PMPI_Bcast (libmpiwrap.c:908)
- ==31319== by 0x400911: main (valgrind-example-mpi.c:20)
- ==31319== Address 0x69291cc is 0 bytes after a block of size 396 alloc'd
- ==31319== at 0x4C27AAA: malloc (vg_replace_malloc.c:291)
- ==31319== by 0x4007BC: main (valgrind-example-mpi.c:8)
- ==31319==
- ==31318== Uninitialised byte(s) found during client check request
- ==31318== at 0x4E3591D: check_mem_is_defined_untyped (libmpiwrap.c:952)
- ==31318== by 0x4E5D06D: PMPI_Bcast (libmpiwrap.c:908)
- ==31318== by 0x400911: main (valgrind-example-mpi.c:20)
- ==31318== Address 0x6929040 is 0 bytes inside a block of size 396 alloc'd
- ==31318== at 0x4C27AAA: malloc (vg_replace_malloc.c:291)
- ==31318== by 0x4007BC: main (valgrind-example-mpi.c:8)
- ==31318==
- ==31318== Unaddressable byte(s) found during client check request
- ==31318== at 0x4E3591D: check_mem_is_defined_untyped (libmpiwrap.c:952)
- ==31318== by 0x4E5D06D: PMPI_Bcast (libmpiwrap.c:908)
- ==31318== by 0x400911: main (valgrind-example-mpi.c:20)
- ==31318== Address 0x69291cc is 0 bytes after a block of size 396 alloc'd
- ==31318== at 0x4C27AAA: malloc (vg_replace_malloc.c:291)
- ==31318== by 0x4007BC: main (valgrind-example-mpi.c:8)
- ==31318==
- ==31318==
- ==31318== HEAP SUMMARY:
- ==31318== in use at exit: 3,172 bytes in 67 blocks
- ==31318== total heap usage: 191 allocs, 124 frees, 81,203 bytes allocated
- ==31318==
- ==31319==
- ==31319== HEAP SUMMARY:
- ==31319== in use at exit: 3,172 bytes in 67 blocks
- ==31319== total heap usage: 175 allocs, 108 frees, 48,435 bytes allocated
- ==31319==
- ==31318== LEAK SUMMARY:
- ==31318== definitely lost: 408 bytes in 3 blocks
- ==31318== indirectly lost: 256 bytes in 1 blocks
- ==31318== possibly lost: 0 bytes in 0 blocks
- ==31318== still reachable: 2,508 bytes in 63 blocks
- ==31318== suppressed: 0 bytes in 0 blocks
- ==31318== Rerun with --leak-check=full to see details of leaked memory
- ==31318==
- ==31318== For counts of detected and suppressed errors, rerun with: -v
- ==31318== Use --track-origins=yes to see where uninitialised values come from
- ==31318== ERROR SUMMARY: 2 errors from 2 contexts (suppressed: 4 from 4)
- ==31319== LEAK SUMMARY:
- ==31319== definitely lost: 408 bytes in 3 blocks
- ==31319== indirectly lost: 256 bytes in 1 blocks
- ==31319== possibly lost: 0 bytes in 0 blocks
- ==31319== still reachable: 2,508 bytes in 63 blocks
- ==31319== suppressed: 0 bytes in 0 blocks
- ==31319== Rerun with --leak-check=full to see details of leaked memory
- ==31319==
- ==31319== For counts of detected and suppressed errors, rerun with: -v
- ==31319== ERROR SUMMARY: 1 errors from 1 contexts (suppressed: 4 from 4)
-
-We can see that Valgrind has reported use of unitialised memory on the
-master process (which reads the array to be broadcasted) and use of
-unaddresable memory on both processes.
-
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-Vampir
-======
-
-Vampir is a commercial trace analysis and visualisation tool. It can
-work with traces in OTF and OTF2 formats. It does not have the
-functionality to collect traces, you need to use a trace collection tool
-(such as [Score-P](score-p.html)) first to collect the
-traces.
-
-
--
-
-Installed versions
-------------------
-
-Version 8.5.0 is currently installed as module
-Vampir/8.5.0 :
-
- $ module load Vampir/8.5.0
- $ vampir &
-
-User manual
------------
-
-You can find the detailed user manual in PDF format in
-$EBROOTVAMPIR/doc/vampir-manual.pdf
-
-References
-----------
-
-1. <https://www.vampir.eu>
-
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-Intel Advisor
-=============
-
-is tool aiming to assist you in vectorization and threading of your
-code. You can use it to profile your application and identify
-loops, that could benefit from vectorization and/or threading
-parallelism.
-
-Installed versions
-------------------
-
-The following versions are currently available on Salomon as modules:
-
- --------- |---|---|-------------
- |---|---|
- |2016 Update 2|Advisor/2016_update2|
- --------- |---|---|-------------
-
-Usage
------
-
-Your program should be compiled with -g switch to include symbol names.
-You should compile with -O2 or higher to see code that is already
-vectorized by the compiler.
-
-Profiling is possible either directly from the GUI, or from command
-line.
-
-To profile from GUI, launch Advisor:
-
- $ advixe-gui
-
-Then select menu File -> New -> Project. Choose a directory to
-save project data to. After clicking OK, Project properties window will
-appear, where you can configure path to your binary, launch arguments,
-working directory etc. After clicking OK, the project is ready.
-
-In the left pane, you can switch between Vectorization and Threading
-workflows. Each has several possible steps which you can execute by
-clicking Collect button. Alternatively, you can click on Command Line,
-to see the command line required to run the analysis directly from
-command line.
-
-References
-----------
-
-1. [Intel® Advisor 2015 Tutorial: Find Where to Add Parallelism - C++
- Sample](https://software.intel.com/en-us/advisorxe_2015_tut_lin_c)
-2. [Product
- page](https://software.intel.com/en-us/intel-advisor-xe)
-3. [Documentation](https://software.intel.com/en-us/intel-advisor-2016-user-guide-linux)
-
-Â
-
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-Intel Compilers
-===============
-
-
-
-The Intel compilers in multiple versions are available, via module
-intel. The compilers include the icc C and C++ compiler and the ifort
-fortran 77/90/95 compiler.
-
- $ module load intel
- $ icc -v
- $ ifort -v
-
-The intel compilers provide for vectorization of the code, via the AVX2
-instructions and support threading parallelization via OpenMP
-
-For maximum performance on the Salomon cluster compute nodes, compile
-your programs using the AVX2 instructions, with reporting where the
-vectorization was used. We recommend following compilation options for
-high performance
-
- $ icc -ipo -O3 -xCORE-AVX2 -qopt-report1 -qopt-report-phase=vec myprog.c mysubroutines.c -o myprog.x
- $ ifort -ipo -O3 -xCORE-AVX2 -qopt-report1 -qopt-report-phase=vec myprog.f mysubroutines.f -o myprog.x
-
-In this example, we compile the program enabling interprocedural
-optimizations between source files (-ipo), aggresive loop optimizations
-(-O3) and vectorization (-xCORE-AVX2)
-
-The compiler recognizes the omp, simd, vector and ivdep pragmas for
-OpenMP parallelization and AVX2 vectorization. Enable the OpenMP
-parallelization by the **-openmp** compiler switch.
-
- $ icc -ipo -O3 -xCORE-AVX2 -qopt-report1 -qopt-report-phase=vec -openmp myprog.c mysubroutines.c -o myprog.x
- $ ifort -ipo -O3 -xCORE-AVX2 -qopt-report1 -qopt-report-phase=vec -openmp myprog.f mysubroutines.f -o myprog.x
-
-Read more
-at <https://software.intel.com/en-us/intel-cplusplus-compiler-16.0-user-and-reference-guide>
-
-Sandy Bridge/Ivy Bridge/Haswell binary compatibility
-----------------------------------------------------
-
-Anselm nodes are currently equipped with Sandy Bridge CPUs, while
-Salomon compute nodes are equipped with Haswell based architecture. The
-UV1 SMP compute server has Ivy Bridge CPUs, which are equivalent to
-Sandy Bridge (only smaller manufacturing technology). >The new
-processors are backward compatible with the Sandy Bridge nodes, so all
-programs that ran on the Sandy Bridge processors, should also run on the
-new Haswell nodes. >To get optimal performance out of the
-Haswell processors a program should make use of the
-special >AVX2 instructions for this processor. One can do
-this by recompiling codes with the compiler
-flags >designated to invoke these instructions. For the
-Intel compiler suite, there are two ways of
-doing >this:
-
-- >Using compiler flag (both for Fortran and C):
- -xCORE-AVX2. This will create a
- binary class="s1">with AVX2 instructions, specifically
- for the Haswell processors. Note that the
- executable >will not run on Sandy Bridge/Ivy
- Bridge nodes.
-- >Using compiler flags (both for Fortran and C):
- -xAVX -axCORE-AVX2. This
- will >generate multiple, feature specific auto-dispatch
- code paths for Intel® processors, if there is >a
- performance benefit. So this binary will run both on Sandy
- Bridge/Ivy Bridge and Haswell >processors. During
- runtime it will be decided which path to follow, dependent on
- which >processor you are running on. In general this
- will result in larger binaries.
-
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-Intel Debugger
-==============
-
-
-
-IDB is no longer available since Intel Parallel Studio 2015
-
-Debugging serial applications
------------------------------
-
-The intel debugger version 13.0 is available, via module intel. The
-debugger works for applications compiled with C and C++ compiler and the
-ifort fortran 77/90/95 compiler. The debugger provides java GUI
-environment. Use [X
-display](../../../get-started-with-it4innovations/accessing-the-clusters/graphical-user-interface/x-window-system/x-window-and-vnc.html)
-for running the GUI.
-
- $ module load intel/2014.06
- $ module load Java
- $ idb
-
-The debugger may run in text mode. To debug in text mode, use
-
- $ idbc
-
-To debug on the compute nodes, module intel must be loaded.
-The GUI on compute nodes may be accessed using the same way as in [the
-GUI
-section](../../../get-started-with-it4innovations/accessing-the-clusters/graphical-user-interface/x-window-system/x-window-and-vnc.html)
-
-Example:
-
- $ qsub -q qexp -l select=1:ncpus=24 -X -I
- qsub: waiting for job 19654.srv11 to start
- qsub: job 19654.srv11 ready
-
- $ module load intel
- $ module load Java
- $ icc -O0 -g myprog.c -o myprog.x
- $ idb ./myprog.x
-
-In this example, we allocate 1 full compute node, compile program
-myprog.c with debugging options -O0 -g and run the idb debugger
-interactively on the myprog.x executable. The GUI access is via X11 port
-forwarding provided by the PBS workload manager.
-
-Debugging parallel applications
--------------------------------
-
-Intel debugger is capable of debugging multithreaded and MPI parallel
-programs as well.
-
-### Small number of MPI ranks
-
-For debugging small number of MPI ranks, you may execute and debug each
-rank in separate xterm terminal (do not forget the [X
-display](../../../get-started-with-it4innovations/accessing-the-clusters/graphical-user-interface/x-window-system/x-window-and-vnc.html)).
-Using Intel MPI, this may be done in following way:
-
- $ qsub -q qexp -l select=2:ncpus=24 -X -I
- qsub: waiting for job 19654.srv11 to start
- qsub: job 19655.srv11 ready
-
- $ module load intel impi
- $ mpirun -ppn 1 -hostfile $PBS_NODEFILE --enable-x xterm -e idbc ./mympiprog.x
-
-In this example, we allocate 2 full compute node, run xterm on each node
-and start idb debugger in command line mode, debugging two ranks of
-mympiprog.x application. The xterm will pop up for each rank, with idb
-prompt ready. The example is not limited to use of Intel MPI
-
-### Large number of MPI ranks
-
-Run the idb debugger from within the MPI debug option. This will cause
-the debugger to bind to all ranks and provide aggregated outputs across
-the ranks, pausing execution automatically just after startup. You may
-then set break points and step the execution manually. Using Intel MPI:
-
- $ qsub -q qexp -l select=2:ncpus=24 -X -I
- qsub: waiting for job 19654.srv11 to start
- qsub: job 19655.srv11 ready
-
- $ module load intel impi
- $ mpirun -n 48 -idb ./mympiprog.x
-
-### Debugging multithreaded application
-
-Run the idb debugger in GUI mode. The menu Parallel contains number of
-tools for debugging multiple threads. One of the most useful tools is
-the **Serialize Execution** tool, which serializes execution of
-concurrent threads for easy orientation and identification of
-concurrency related bugs.
-
-Further information
--------------------
-
-Exhaustive manual on idb features and usage is published at Intel
-website,
-<https://software.intel.com/sites/products/documentation/doclib/iss/2013/compiler/cpp-lin/>
-
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-Intel Inspector
-===============
-
-Intel Inspector is a dynamic memory and threading error checking tool
-for C/C++/Fortran applications. It can detect issues such as memory
-leaks, invalid memory references, uninitalized variables, race
-conditions, deadlocks etc.
-
-Installed versions
-------------------
-
-The following versions are currently available on Salomon as modules:
-
- --------- |---|---|---------------
- |---|---|
- |2016 Update 1|Inspector/2016_update1|
- --------- |---|---|---------------
-
-Usage
------
-
-Your program should be compiled with -g switch to include symbol names.
-Optimizations can be turned on.
-
-Debugging is possible either directly from the GUI, or from command
-line.
-
-### GUI mode
-
-To debug from GUI, launch Inspector:
-
- $ inspxe-gui &
-
-Then select menu File -> New -> Project. Choose a directory to
-save project data to. After clicking OK, Project properties window will
-appear, where you can configure path to your binary, launch arguments,
-working directory etc. After clicking OK, the project is ready.
-
-In the main pane, you can start a predefined analysis type or define
-your own. Click Start to start the analysis. Alternatively, you can
-click on Command Line, to see the command line required to run the
-analysis directly from command line.
-
-### Batch mode
-
-Analysis can be also run from command line in batch mode. Batch mode
-analysis is run with command inspxe-cl.
-To obtain the required parameters, either consult the documentation or
-you can configure the analysis in the GUI and then click "Command Line"
-button in the lower right corner to the respective command line.
-
-Results obtained from batch mode can be then viewed in the GUI by
-selecting File -> Open -> Result...
-
-References
-----------
-
-1. [Product
- page](https://software.intel.com/en-us/intel-inspector-xe)
-2. [Documentation and Release
- Notes](https://software.intel.com/en-us/intel-inspector-xe-support/documentation)
-3. [Tutorials](https://software.intel.com/en-us/articles/inspectorxe-tutorials)
-
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-Intel IPP
-=========
-
-
-
-Intel Integrated Performance Primitives
----------------------------------------
-
-Intel Integrated Performance Primitives, version 9.0.1, compiled for
-AVX2 vector instructions is available, via module ipp. The IPP is a very
-rich library of highly optimized algorithmic building blocks for media
-and data applications. This includes signal, image and frame processing
-algorithms, such as FFT, FIR, Convolution, Optical Flow, Hough
-transform, Sum, MinMax, as well as cryptographic functions, linear
-algebra functions and many more.
-
-Check out IPP before implementing own math functions for data
-processing, it is likely already there.
-
- $ module load ipp
-
-The module sets up environment variables, required for linking and
-running ipp enabled applications.
-
-IPP example
------------
-
- #include "ipp.h"
- #include <stdio.h>
- int main(int argc, char* argv[])
- {
- const IppLibraryVersion *lib;
- Ipp64u fm;
- IppStatus status;
-
- status= ippInit(); //IPP initialization with the best optimization layer
- if( status != ippStsNoErr ) {
- printf("IppInit() Error:n");
- printf("%sn", ippGetStatusString(status) );
- return -1;
- }
-
- //Get version info
- lib = ippiGetLibVersion();
- printf("%s %sn", lib->Name, lib->Version);
-
- //Get CPU features enabled with selected library level
- fm=ippGetEnabledCpuFeatures();
- printf("SSE :%cn",(fm>1)&1?'Y':'N');
- printf("SSE2 :%cn",(fm>2)&1?'Y':'N');
- printf("SSE3 :%cn",(fm>3)&1?'Y':'N');
- printf("SSSE3 :%cn",(fm>4)&1?'Y':'N');
- printf("SSE41 :%cn",(fm>6)&1?'Y':'N');
- printf("SSE42 :%cn",(fm>7)&1?'Y':'N');
- printf("AVX :%cn",(fm>8)&1 ?'Y':'N');
- printf("AVX2 :%cn", (fm>15)&1 ?'Y':'N' );
- printf("----------n");
- printf("OS Enabled AVX :%cn", (fm>9)&1 ?'Y':'N');
- printf("AES :%cn", (fm>10)&1?'Y':'N');
- printf("CLMUL :%cn", (fm>11)&1?'Y':'N');
- printf("RDRAND :%cn", (fm>13)&1?'Y':'N');
- printf("F16C :%cn", (fm>14)&1?'Y':'N');
-
- return 0;
- }
-
-Â Compile above example, using any compiler and the ipp module.
-
- $ module load intel
- $ module load ipp
-
- $ icc testipp.c -o testipp.x -lippi -lipps -lippcore
-
-You will need the ipp module loaded to run the ipp enabled executable.
-This may be avoided, by compiling library search paths into the
-executable
-
- $ module load intel
- $ module load ipp
-
- $ icc testipp.c -o testipp.x -Wl,-rpath=$LIBRARY_PATH -lippi -lipps -lippcore
-
-Code samples and documentation
-------------------------------
-
-Intel provides number of [Code Samples for
-IPP](https://software.intel.com/en-us/articles/code-samples-for-intel-integrated-performance-primitives-library),
-illustrating use of IPP.
-
-Read full documentation on IPP [on Intel
-website,](http://software.intel.com/sites/products/search/search.php?q=&x=15&y=6&product=ipp&version=7.1&docos=lin)
-in particular the [IPP Reference
-manual.](http://software.intel.com/sites/products/documentation/doclib/ipp_sa/71/ipp_manual/index.htm)
-
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-Intel MKL
-=========
-
-
-
-Intel Math Kernel Library
--------------------------
-
-Intel Math Kernel Library (Intel MKL) is a library of math kernel
-subroutines, extensively threaded and optimized for maximum performance.
-Intel MKL provides these basic math kernels:
-
--
-
-
-
- BLAS (level 1, 2, and 3) and LAPACK linear algebra routines,
- offering vector, vector-matrix, and matrix-matrix operations.
--
-
-
-
- The PARDISO direct sparse solver, an iterative sparse solver,
- and supporting sparse BLAS (level 1, 2, and 3) routines for solving
- sparse systems of equations.
--
-
-
-
- ScaLAPACK distributed processing linear algebra routines for
- Linux* and Windows* operating systems, as well as the Basic Linear
- Algebra Communications Subprograms (BLACS) and the Parallel Basic
- Linear Algebra Subprograms (PBLAS).
--
-
-
-
- Fast Fourier transform (FFT) functions in one, two, or three
- dimensions with support for mixed radices (not limited to sizes that
- are powers of 2), as well as distributed versions of
- these functions.
--
-
-
-
- Vector Math Library (VML) routines for optimized mathematical
- operations on vectors.
--
-
-
-
- Vector Statistical Library (VSL) routines, which offer
- high-performance vectorized random number generators (RNG) for
- several probability distributions, convolution and correlation
- routines, and summary statistics functions.
--
-
-
-
- Data Fitting Library, which provides capabilities for
- spline-based approximation of functions, derivatives and integrals
- of functions, and search.
-- Extended Eigensolver, a shared memory version of an eigensolver
- based on the Feast Eigenvalue Solver.
-
-For details see the [Intel MKL Reference
-Manual](http://software.intel.com/sites/products/documentation/doclib/mkl_sa/11/mklman/index.htm).
-
-Intel MKL version 11.2.3.187 is available on the cluster
-
- $ module load imkl
-
-The module sets up environment variables, required for linking and
-running mkl enabled applications. The most important variables are the
-$MKLROOT, $CPATH, $LD_LIBRARY_PATH and $MKL_EXAMPLES
-
-Intel MKL library may be linked using any compiler.
-With intel compiler use -mkl option to link default threaded MKL.
-
-### Interfaces
-
-Intel MKL library provides number of interfaces. The fundamental once
-are the LP64 and ILP64. The Intel MKL ILP64 libraries use the 64-bit
-integer type (necessary for indexing large arrays, with more than
-231^-1 elements), whereas the LP64 libraries index arrays with the
-32-bit integer type.
-
- |Interface|Integer type|
- ----- |---|---|-------------------------------------
- |LP64|32-bit, int, integer(kind=4), MPI_INT|
- ILP64 64-bit, long int, integer(kind=8), MPI_INT64
-
-### Linking
-
-Linking Intel MKL libraries may be complex. Intel [mkl link line
-advisor](http://software.intel.com/en-us/articles/intel-mkl-link-line-advisor)
-helps. See also [examples](intel-mkl.html#examples) below.
-
-You will need the mkl module loaded to run the mkl enabled executable.
-This may be avoided, by compiling library search paths into the
-executable. Include rpath on the compile line:
-
- $ icc .... -Wl,-rpath=$LIBRARY_PATH ...
-
-### Threading
-
-Advantage in using Intel MKL library is that it brings threaded
-parallelization to applications that are otherwise not parallel.
-
-For this to work, the application must link the threaded MKL library
-(default). Number and behaviour of MKL threads may be controlled via the
-OpenMP environment variables, such as OMP_NUM_THREADS and
-KMP_AFFINITY. MKL_NUM_THREADS takes precedence over OMP_NUM_THREADS
-
- $ export OMP_NUM_THREADS=24
- $ export KMP_AFFINITY=granularity=fine,compact,1,0
-
-The application will run with 24 threads with affinity optimized for
-fine grain parallelization.
-
-Examples
-------------
-
-Number of examples, demonstrating use of the Intel MKL library and its
-linking is available on clusters, in the $MKL_EXAMPLES directory. In
-the examples below, we demonstrate linking Intel MKL to Intel and GNU
-compiled program for multi-threaded matrix multiplication.
-
-### Working with examples
-
- $ module load intel
- $ module load imkl
- $ cp -a $MKL_EXAMPLES/cblas /tmp/
- $ cd /tmp/cblas
-
- $ make sointel64 function=cblas_dgemm
-
-In this example, we compile, link and run the cblas_dgemm example,
-demonstrating use of MKL example suite installed on clusters.
-
-### Example: MKL and Intel compiler
-
- $ module load intel
- $ module load imkl
- $ cp -a $MKL_EXAMPLES/cblas /tmp/
- $ cd /tmp/cblas
- $
- $ icc -w source/cblas_dgemmx.c source/common_func.c -mkl -o cblas_dgemmx.x
- $ ./cblas_dgemmx.x data/cblas_dgemmx.d
-
-In this example, we compile, link and run the cblas_dgemm example,
-demonstrating use of MKL with icc -mkl option. Using the -mkl option is
-equivalent to:
-
- $ icc -w source/cblas_dgemmx.c source/common_func.c -o cblas_dgemmx.x
- -I$MKL_INC_DIR -L$MKL_LIB_DIR -lmkl_intel_lp64 -lmkl_intel_thread -lmkl_core -liomp5
-
-In this example, we compile and link the cblas_dgemm example, using
-LP64 interface to threaded MKL and Intel OMP threads implementation.
-
-### Example: Intel MKL and GNU compiler
-
- $ module load GCC
- $ module load imkl
- $ cp -a $MKL_EXAMPLES/cblas /tmp/
- $ cd /tmp/cblas
-
- $ gcc -w source/cblas_dgemmx.c source/common_func.c -o cblas_dgemmx.x
- -lmkl_intel_lp64 -lmkl_gnu_thread -lmkl_core -lgomp -lm
-
- $ ./cblas_dgemmx.x data/cblas_dgemmx.d
-
-In this example, we compile, link and run the cblas_dgemm example,
-using LP64 interface to threaded MKL and gnu OMP threads implementation.
-
-MKL and MIC accelerators
-------------------------
-
-The Intel MKL is capable to automatically offload the computations o the
-MIC accelerator. See section [Intel Xeon
-Phi](../intel-xeon-phi.html) for details.
-
-LAPACKE C Interface
--------------------
-
-MKL includes LAPACKE C Interface to LAPACK. For some reason, although
-Intel is the author of LAPACKE, the LAPACKE header files are not present
-in MKL. For this reason, we have prepared
-LAPACKE module, which includes Intel's LAPACKE
-headers from official LAPACK, which you can use to compile code using
-LAPACKE interface against MKL.
-
-Further reading
----------------
-
-Read more on [Intel
-website](http://software.intel.com/en-us/intel-mkl), in
-particular the [MKL users
-guide](https://software.intel.com/en-us/intel-mkl/documentation/linux).
-
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-Intel Parallel Studio
-=====================
-
-
-
-The Salomon cluster provides following elements of the Intel Parallel
-Studio XE
-
- Intel Parallel Studio XE
- -------------------------------------------------
- Intel Compilers
- Intel Debugger
- Intel MKL Library
- Intel Integrated Performance Primitives Library
- Intel Threading Building Blocks Library
- Intel Trace Analyzer and Collector
- Intel Advisor
- Intel Inspector
-
-Intel compilers
----------------
-
-The Intel compilers version 131.3 are available, via module
-iccifort/2013.5.192-GCC-4.8.3. The compilers include the icc C and C++
-compiler and the ifort fortran 77/90/95 compiler.
-
- $ module load intel
- $ icc -v
- $ ifort -v
-
-Read more at the [Intel Compilers](intel-compilers.html)
-page.
-
-Intel debugger
---------------
-
-IDB is no longer available since Parallel Studio 2015.
-
-Â The intel debugger version 13.0 is available, via module intel. The
-debugger works for applications compiled with C and C++ compiler and the
-ifort fortran 77/90/95 compiler. The debugger provides java GUI
-environment. Use [X
-display](../../../get-started-with-it4innovations/accessing-the-clusters/graphical-user-interface/x-window-system/x-window-and-vnc.html)
-for running the GUI.
-
- $ module load intel
- $ idb
-
-Read more at the [Intel Debugger](intel-debugger.html)
-page.
-
-Intel Math Kernel Library
--------------------------
-
-Intel Math Kernel Library (Intel MKL) is a library of math kernel
-subroutines, extensively threaded and optimized for maximum performance.
-Intel MKL unites and provides these basic components: BLAS, LAPACK,
-ScaLapack, PARDISO, FFT, VML, VSL, Data fitting, Feast Eigensolver and
-many more.
-
- $ module load imkl
-
-Read more at the [Intel MKL](intel-mkl.html) page.
-
-Intel Integrated Performance Primitives
----------------------------------------
-
-Intel Integrated Performance Primitives, version 7.1.1, compiled for AVX
-is available, via module ipp. The IPP is a library of highly optimized
-algorithmic building blocks for media and data applications. This
-includes signal, image and frame processing algorithms, such as FFT,
-FIR, Convolution, Optical Flow, Hough transform, Sum, MinMax and many
-more.
-
- $ module load ipp
-
-Read more at the [Intel
-IPP](intel-integrated-performance-primitives.html) page.
-
-Intel Threading Building Blocks
--------------------------------
-
-Intel Threading Building Blocks (Intel TBB) is a library that supports
-scalable parallel programming using standard ISO C++ code. It does not
-require special languages or compilers. It is designed to promote
-scalable data parallel programming. Additionally, it fully supports
-nested parallelism, so you can build larger parallel components from
-smaller parallel components. To use the library, you specify tasks, not
-threads, and let the library map tasks onto threads in an efficient
-manner.
-
- $ module load tbb
-
-Read more at the [Intel TBB](intel-tbb.html) page.
-
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-Intel TBB
-=========
-
-
-
-Intel Threading Building Blocks
--------------------------------
-
-Intel Threading Building Blocks (Intel TBB) is a library that supports
-scalable parallel programming using standard ISO C++ code. It does not
-require special languages or compilers. To use the library, you specify
-tasks, not threads, and let the library map tasks onto threads in an
-efficient manner. The tasks are executed by a runtime scheduler and may
-be offloaded to [MIC
-accelerator](../intel-xeon-phi.html).
-
-Intel TBB version 4.3.5.187 is available on the cluster.
-
- $ module load tbb
-
-The module sets up environment variables, required for linking and
-running tbb enabled applications.
-
-Link the tbb library, using -ltbb
-
-Examples
---------
-
-Number of examples, demonstrating use of TBB and its built-in schedulerÂ
-is available on Anselm, in the $TBB_EXAMPLES directory.
-
- $ module load intel
- $ module load tbb
- $ cp -a $TBB_EXAMPLES/common $TBB_EXAMPLES/parallel_reduce /tmp/
- $ cd /tmp/parallel_reduce/primes
- $ icc -O2 -DNDEBUG -o primes.x main.cpp primes.cpp -ltbb
- $ ./primes.x
-
-In this example, we compile, link and run the primes example,
-demonstrating use of parallel task-based reduce in computation of prime
-numbers.
-
-You will need the tbb module loaded to run the tbb enabled executable.
-This may be avoided, by compiling library search paths into the
-executable.
-
- $ icc -O2 -o primes.x main.cpp primes.cpp -Wl,-rpath=$LIBRARY_PATH -ltbb
-
-Further reading
----------------
-
-Read more on Intel website,
-<http://software.intel.com/sites/products/documentation/doclib/tbb_sa/help/index.htm>
-
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-Intel Trace Analyzer and Collector
-==================================
-
-Intel Trace Analyzer and Collector (ITAC) is a tool to collect and
-graphicaly analyze behaviour of MPI applications. It helps you to
-analyze communication patterns of your application, identify hotspots,
-perform correctnes checking (identify deadlocks, data corruption etc),
-simulate how your application would run on a different interconnect.Â
-
-ITAC is a offline analysis tool - first you run your application to
-collect a trace file, then you can open the trace in a GUI analyzer to
-view it.
-
-Installed version
------------------
-
-Currently on Salomon is version 9.1.2.024 available as moduleÂ
-itac/9.1.2.024
-
-Collecting traces
------------------
-
-ITAC can collect traces from applications that are using Intel MPI. To
-generate a trace, simply add -trace option to your mpirun command :
-
- $ module load itac/9.1.2.024
- $ mpirun -trace myapp
-
-The trace will be saved in file myapp.stf in the current directory.
-
-Viewing traces
---------------
-
-To view and analyze the trace, open ITAC GUI in a [graphical
-environment](../../../get-started-with-it4innovations/accessing-the-clusters/graphical-user-interface/x-window-system/x-window-and-vnc.html)
-:
-
- $ module load itac/9.1.2.024
- $ traceanalyzer
-
-The GUI will launch and you can open the produced *.stf file.
-
-
-
-Please refer to Intel documenation about usage of the GUI tool.
-
-References
-----------
-
-1. [Getting Started with Intel® Trace Analyzer and
- Collector](https://software.intel.com/en-us/get-started-with-itac-for-linux)
-2. [Intel® Trace Analyzer and Collector -
- Documentation](http://Intel®%20Trace%20Analyzer%20and%20Collector%20-%20Documentation)
-
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-Intel Xeon Phi
-==============
-
-A guide to Intel Xeon Phi usage
-
-
-
-Intel Xeon Phi accelerator can be programmed in several modes. The
-default mode on the cluster is offload mode, but all modes described in
-this document are supported.
-
-Intel Utilities for Xeon Phi
-----------------------------
-
-To get access to a compute node with Intel Xeon Phi accelerator, use the
-PBS interactive session
-
- $ qsub -I -q qprod -l select=1:ncpus=24:accelerator=True:naccelerators=2:accelerator_model=phi7120 -A NONE-0-0
-
-To set up the environment module "intel" has to be loaded, without
-specifying the version, default version is loaded (at time of writing
-this, it's 2015b)
-
- $ module load intel
-
-Information about the hardware can be obtained by running
-the micinfo program on the host.
-
- $ /usr/bin/micinfo
-
-The output of the "micinfo" utility executed on one of the cluster node
-is as follows. (note: to get PCIe related details the command has to be
-run with root privileges)
-
- MicInfo Utility Log
- Created Mon Aug 17 13:55:59 2015
-
- System Info
- HOST OS : Linux
- OS Version : 2.6.32-504.16.2.el6.x86_64
- Driver Version : 3.4.1-1
- MPSS Version : 3.4.1
- Host Physical Memory : 131930 MB
-
- Device No: 0, Device Name: mic0
-
- Version
- Flash Version : 2.1.02.0390
- SMC Firmware Version : 1.16.5078
- SMC Boot Loader Version : 1.8.4326
- uOS Version : 2.6.38.8+mpss3.4.1
- Device Serial Number : ADKC44601414
-
- Board
- Vendor ID : 0x8086
- Device ID : 0x225c
- Subsystem ID : 0x7d95
- Coprocessor Stepping ID : 2
- PCIe Width : x16
- PCIe Speed : 5 GT/s
- PCIe Max payload size : 256 bytes
- PCIe Max read req size : 512 bytes
- Coprocessor Model : 0x01
- Coprocessor Model Ext : 0x00
- Coprocessor Type : 0x00
- Coprocessor Family : 0x0b
- Coprocessor Family Ext : 0x00
- Coprocessor Stepping : C0
- Board SKU : C0PRQ-7120 P/A/X/D
- ECC Mode : Enabled
- SMC HW Revision : Product 300W Passive CS
-
- Cores
- Total No of Active Cores : 61
- Voltage : 1007000 uV
- Frequency : 1238095 kHz
-
- Thermal
- Fan Speed Control : N/A
- Fan RPM : N/A
- Fan PWM : N/A
- Die Temp : 60 C
-
- GDDR
- GDDR Vendor : Samsung
- GDDR Version : 0x6
- GDDR Density : 4096 Mb
- GDDR Size : 15872 MB
- GDDR Technology : GDDR5
- GDDR Speed : 5.500000 GT/s
- GDDR Frequency : 2750000 kHz
- GDDR Voltage : 1501000 uV
-
- Device No: 1, Device Name: mic1
-
- Version
- Flash Version : 2.1.02.0390
- SMC Firmware Version : 1.16.5078
- SMC Boot Loader Version : 1.8.4326
- uOS Version : 2.6.38.8+mpss3.4.1
- Device Serial Number : ADKC44500454
-
- Board
- Vendor ID : 0x8086
- Device ID : 0x225c
- Subsystem ID : 0x7d95
- Coprocessor Stepping ID : 2
- PCIe Width : x16
- PCIe Speed : 5 GT/s
- PCIe Max payload size : 256 bytes
- PCIe Max read req size : 512 bytes
- Coprocessor Model : 0x01
- Coprocessor Model Ext : 0x00
- Coprocessor Type : 0x00
- Coprocessor Family : 0x0b
- Coprocessor Family Ext : 0x00
- Coprocessor Stepping : C0
- Board SKU : C0PRQ-7120 P/A/X/D
- ECC Mode : Enabled
- SMC HW Revision : Product 300W Passive CS
-
- Cores
- Total No of Active Cores : 61
- Voltage : 998000 uV
- Frequency : 1238095 kHz
-
- Thermal
- Fan Speed Control : N/A
- Fan RPM : N/A
- Fan PWM : N/A
- Die Temp : 59 C
-
- GDDR
- GDDR Vendor : Samsung
- GDDR Version : 0x6
- GDDR Density : 4096 Mb
- GDDR Size : 15872 MB
- GDDR Technology : GDDR5
- GDDR Speed : 5.500000 GT/s
- GDDR Frequency : 2750000 kHz
- GDDR Voltage : 1501000 uV
-
-Offload Mode
-------------
-
-To compile a code for Intel Xeon Phi a MPSS stack has to be installed on
-the machine where compilation is executed. Currently the MPSS stack is
-only installed on compute nodes equipped with accelerators.
-
- $ qsub -I -q qprod -l select=1:ncpus=24:accelerator=True:naccelerators=2:accelerator_model=phi7120 -A NONE-0-0
- $ module load intel
-
-For debugging purposes it is also recommended to set environment
-variable "OFFLOAD_REPORT". Value can be set from 0 to 3, where higher
-number means more debugging information.
-
- export OFFLOAD_REPORT=3
-
-A very basic example of code that employs offload programming technique
-is shown in the next listing. Please note that this code is sequential
-and utilizes only single core of the accelerator.
-
- $ vim source-offload.cpp
-
- #include <iostream>
-
- int main(int argc, char* argv[])
- {
- Â Â Â const int niter = 100000;
- Â Â Â double result = 0;
-
- Â #pragma offload target(mic)
- Â Â Â for (int i = 0; i < niter; ++i) {
- Â Â Â Â Â Â Â const double t = (i + 0.5) / niter;
- Â Â Â Â Â Â Â result += 4.0 / (t * t + 1.0);
- Â Â Â }
- Â Â Â result /= niter;
- Â Â Â std::cout << "Pi ~ " << result << 'n';
- }
-
-To compile a code using Intel compiler run
-
- $ icc source-offload.cpp -o bin-offload
-
-To execute the code, run the following command on the host
-
- ./bin-offload
-
-### Parallelization in Offload Mode Using OpenMP
-
-One way of paralelization a code for Xeon Phi is using OpenMP
-directives. The following example shows code for parallel vector
-addition.Â
-
- $ vim ./vect-add
-
- #include <stdio.h>
-
- typedef int T;
-
- #define SIZE 1000
-
- #pragma offload_attribute(push, target(mic))
- T in1[SIZE];
- T in2[SIZE];
- T res[SIZE];
- #pragma offload_attribute(pop)
-
- // MIC function to add two vectors
- __attribute__((target(mic))) add_mic(T *a, T *b, T *c, int size) {
- Â int i = 0;
- Â #pragma omp parallel for
- Â Â Â for (i = 0; i < size; i++)
- Â Â Â Â Â c[i] = a[i] + b[i];
- }
-
- // CPU function to add two vectors
- void add_cpu (T *a, T *b, T *c, int size) {
- Â int i;
- Â for (i = 0; i < size; i++)
- Â Â Â c[i] = a[i] + b[i];
- }
-
- // CPU function to generate a vector of random numbers
- void random_T (T *a, int size) {
- Â int i;
- Â for (i = 0; i < size; i++)
- Â Â Â a[i] = rand() % 10000; // random number between 0 and 9999
- }
-
- // CPU function to compare two vectors
- int compare(T *a, T *b, T size ){
- Â int pass = 0;
- Â int i;
- Â for (i = 0; i < size; i++){
- Â Â Â if (a[i] != b[i]) {
- Â Â Â Â Â printf("Value mismatch at location %d, values %d and %dn",i, a[i], b[i]);
- Â Â Â Â Â pass = 1;
- Â Â Â }
- Â }
- Â if (pass == 0) printf ("Test passedn"); else printf ("Test Failedn");
- Â return pass;
- }
-
- int main()
- {
- Â int i;
- Â random_T(in1, SIZE);
- Â random_T(in2, SIZE);
-
- Â #pragma offload target(mic) in(in1,in2)Â inout(res)
- Â {
-
- Â Â Â // Parallel loop from main function
- Â Â Â #pragma omp parallel for
- Â Â Â for (i=0; i<SIZE; i++)
- Â Â Â Â Â res[i] = in1[i] + in2[i];
-
- Â Â Â // or parallel loop is called inside the function
- Â Â Â add_mic(in1, in2, res, SIZE);
-
- Â }
-
- Â //Check the results with CPU implementation
- Â T res_cpu[SIZE];
- Â add_cpu(in1, in2, res_cpu, SIZE);
- Â compare(res, res_cpu, SIZE);
-
- }
-
-During the compilation Intel compiler shows which loops have been
-vectorized in both host and accelerator. This can be enabled with
-compiler option "-vec-report2". To compile and execute the code run
-
- $ icc vect-add.c -openmp_report2 -vec-report2 -o vect-add
-
- $ ./vect-add
-
-Some interesting compiler flags useful not only for code debugging are:
-
-Debugging
-Â openmp_report[0|1|2] - controls the compiler based vectorization
-diagnostic level
-Â vec-report[0|1|2] - controls the OpenMP parallelizer diagnostic
-level
-
-Performance ooptimization
-Â xhost - FOR HOST ONLY - to generate AVX (Advanced Vector Extensions)
-instructions.
-
-Automatic Offload using Intel MKL Library
------------------------------------------
-
-Intel MKL includes an Automatic Offload (AO) feature that enables
-computationally intensive MKL functions called in user code to benefit
-from attached Intel Xeon Phi coprocessors automatically and
-transparently.
-
-Behavioural of automatic offload mode is controlled by functions called
-within the program or by environmental variables. Complete list of
-controls is listed [
-here](http://software.intel.com/sites/products/documentation/doclib/mkl_sa/11/mkl_userguide_lnx/GUID-3DC4FC7D-A1E4-423D-9C0C-06AB265FFA86.htm).
-
-The Automatic Offload may be enabled by either an MKL function call
-within the code:
-
- mkl_mic_enable();
-
-or by setting environment variable
-
- $ export MKL_MIC_ENABLE=1
-
-To get more information about automatic offload please refer to "[Using
-Intel® MKL Automatic Offload on Intel ® Xeon Phi™
-Coprocessors](http://software.intel.com/sites/default/files/11MIC42_How_to_Use_MKL_Automatic_Offload_0.pdf)"
-white paper or [ Intel MKL
-documentation](https://software.intel.com/en-us/articles/intel-math-kernel-library-documentation).
-
-### Automatic offload example #1
-
-Following example show how to automatically offload an SGEMM (single
-precision - g dir="auto">eneral matrix multiply) function to
-MIC coprocessor.
-
-At first get an interactive PBS session on a node with MIC accelerator
-and load "intel" module that automatically loads "mkl" module as well.
-
- $ qsub -I -q qprod -l select=1:ncpus=24:accelerator=True:naccelerators=2:accelerator_model=phi7120 -A NONE-0-0
- $ module load intel
-
-Â The code can be copied to a file and compiled without any necessary
-modification.Â
-
- $ vim sgemm-ao-short.c
-
-`
-#include <stdio.h>
-#include <stdlib.h>
-#include <malloc.h>
-#include <stdint.h>
-
-#include "mkl.h"
-
-int main(int argc, char **argv)
-{
-Â Â Â Â Â Â Â float *A, *B, *C; /* Matrices */
-
-Â Â Â Â Â Â Â MKL_INT N = 2560; /* Matrix dimensions */
-Â Â Â Â Â Â Â MKL_INT LD = N; /* Leading dimension */
-Â Â Â Â Â Â Â int matrix_bytes; /* Matrix size in bytes */
-Â Â Â Â Â Â Â int matrix_elements; /* Matrix size in elements */
-
-Â Â Â Â Â Â Â float alpha = 1.0, beta = 1.0; /* Scaling factors */
-Â Â Â Â Â Â Â char transa = 'N', transb = 'N'; /* Transposition options */
-
-Â Â Â Â Â Â Â int i, j; /* Counters */
-
-Â Â Â Â Â Â Â matrix_elements = N * N;
-Â Â Â Â Â Â Â matrix_bytes = sizeof(float) * matrix_elements;
-
-Â Â Â Â Â Â Â /* Allocate the matrices */
-Â Â Â Â Â Â Â A = malloc(matrix_bytes); B = malloc(matrix_bytes); C = malloc(matrix_bytes);
-
-Â Â Â Â Â Â Â /* Initialize the matrices */
-Â Â Â Â Â Â Â for (i = 0; i < matrix_elements; i++) {
-Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â A[i] = 1.0; B[i] = 2.0; C[i] = 0.0;
-Â Â Â Â Â Â Â }
-
-Â Â Â Â Â Â Â printf("Computing SGEMM on the hostn");
-Â Â Â Â Â Â Â sgemm(&transa, &transb, &N, &N, &N, &alpha, A, &N, B, &N, &beta, C, &N);
-
-Â Â Â Â Â Â Â printf("Enabling Automatic Offloadn");
-Â Â Â Â Â Â Â /* Alternatively, set environment variable MKL_MIC_ENABLE=1 */
-Â Â Â Â Â Â Â mkl_mic_enable();
-Â Â Â Â Â Â Â
-Â Â Â Â Â Â Â int ndevices = mkl_mic_get_device_count(); /* Number of MIC devices */
-       printf("Automatic Offload enabled: %d MIC devices presentn",  ndevices);
-
-Â Â Â Â Â Â Â printf("Computing SGEMM with automatic workdivisionn");
-Â Â Â Â Â Â Â sgemm(&transa, &transb, &N, &N, &N, &alpha, A, &N, B, &N, &beta, C, &N);
-
-Â Â Â Â Â Â Â /* Free the matrix memory */
-Â Â Â Â Â Â Â free(A); free(B); free(C);
-
-Â Â Â Â Â Â Â printf("Donen");
-
-Â Â Â return 0;
-}
-`
-
-Please note: This example is simplified version of an example from MKL.
-The expanded version can be found here:
-$MKL_EXAMPLES/mic_ao/blasc/source/sgemm.c**
-
-To compile a code using Intel compiler use:
-
- $ icc -mkl sgemm-ao-short.c -o sgemm
-
-For debugging purposes enable the offload report to see more information
-about automatic offloading.
-
- $ export OFFLOAD_REPORT=2
-
-The output of a code should look similar to following listing, where
-lines starting with [MKL] are generated by offload reporting:
-
- [user@r31u03n799 ~]$ ./sgemm
- Computing SGEMM on the host
- Enabling Automatic Offload
- Automatic Offload enabled: 2 MIC devices present
- Computing SGEMM with automatic workdivision
- [MKL] [MIC --] [AO Function]Â Â Â SGEMM
- [MKL] [MIC --] [AO SGEMM Workdivision]Â Â Â 0.44 0.28 0.28
- [MKL] [MIC 00] [AO SGEMM CPU Time]Â Â Â 0.252427 seconds
- [MKL] [MIC 00] [AO SGEMM MIC Time]Â Â Â 0.091001 seconds
- [MKL] [MIC 00] [AO SGEMM CPU->MIC Data]Â Â Â 34078720 bytes
- [MKL] [MIC 00] [AO SGEMM MIC->CPU Data]Â Â Â 7864320 bytes
- [MKL] [MIC 01] [AO SGEMM CPU Time]Â Â Â 0.252427 seconds
- [MKL] [MIC 01] [AO SGEMM MIC Time]Â Â Â 0.094758 seconds
- [MKL] [MIC 01] [AO SGEMM CPU->MIC Data]Â Â Â 34078720 bytes
- [MKL] [MIC 01] [AO SGEMM MIC->CPU Data]Â Â Â 7864320 bytes
- Done
-
-Behavioral of automatic offload mode is controlled by functions called
-within the program or by environmental variables. Complete list of
-controls is listed [
-here](http://software.intel.com/sites/products/documentation/doclib/mkl_sa/11/mkl_userguide_lnx/GUID-3DC4FC7D-A1E4-423D-9C0C-06AB265FFA86.htm).
-
-To get more information about automatic offload please refer to "[Using
-Intel® MKL Automatic Offload on Intel ® Xeon Phi™
-Coprocessors](http://software.intel.com/sites/default/files/11MIC42_How_to_Use_MKL_Automatic_Offload_0.pdf)"
-white paper or [ Intel MKL
-documentation](https://software.intel.com/en-us/articles/intel-math-kernel-library-documentation).
-
-### Automatic offload example #2
-
-In this example, we will demonstrate automatic offload control via an
-environment vatiable MKL_MIC_ENABLE. The function DGEMM will be
-offloaded.
-
-At first get an interactive PBS session on a node with MIC accelerator.
-
- $ qsub -I -q qprod -l select=1:ncpus=24:accelerator=True:naccelerators=2:accelerator_model=phi7120 -A NONE-0-0
-
-Once in, we enable the offload and run the Octave software. In octave,
-we generate two large random matrices and let them multiply together.
-
- $ export MKL_MIC_ENABLE=1
- $ export OFFLOAD_REPORT=2
- $ module load Octave/3.8.2-intel-2015b
-
- $ octave -q
- octave:1> A=rand(10000);
- octave:2> B=rand(10000);
- octave:3> C=A*B;
- [MKL] [MIC --] [AO Function]Â Â Â DGEMM
- [MKL] [MIC --] [AO DGEMM Workdivision]Â Â Â 0.14 0.43 0.43
- [MKL] [MIC 00] [AO DGEMM CPU Time]Â Â Â 3.814714 seconds
- [MKL] [MIC 00] [AO DGEMM MIC Time]Â Â Â 2.781595 seconds
- [MKL] [MIC 00] [AO DGEMM CPU->MIC Data]Â Â Â 1145600000 bytes
- [MKL] [MIC 00] [AO DGEMM MIC->CPU Data]Â Â Â 1382400000 bytes
- [MKL] [MIC 01] [AO DGEMM CPU Time]Â Â Â 3.814714 seconds
- [MKL] [MIC 01] [AO DGEMM MIC Time]Â Â Â 2.843016 seconds
- [MKL] [MIC 01] [AO DGEMM CPU->MIC Data]Â Â Â 1145600000 bytes
- [MKL] [MIC 01] [AO DGEMM MIC->CPU Data]Â Â Â 1382400000 bytes
- octave:4> exit
-
-On the example above we observe, that the DGEMM function workload was
-split over CPU, MIC 0 and MIC 1, in the ratio 0.14 0.43 0.43. The matrix
-multiplication was done on the CPU, accelerated by two Xeon Phi
-accelerators.
-
-Native Mode
------------
-
-In the native mode a program is executed directly on Intel Xeon Phi
-without involvement of the host machine. Similarly to offload mode, the
-code is compiled on the host computer with Intel compilers.
-
-To compile a code user has to be connected to a compute with MIC and
-load Intel compilers module. To get an interactive session on a compute
-node with an Intel Xeon Phi and load the module use following commands:Â
-
- $ qsub -I -q qprod -l select=1:ncpus=24:accelerator=True:naccelerators=2:accelerator_model=phi7120 -A NONE-0-0
-
- $ module load intel
-
-Please note that particular version of the Intel module is specified.
-This information is used later to specify the correct library paths.
-
-To produce a binary compatible with Intel Xeon Phi architecture user has
-to specify "-mmic" compiler flag. Two compilation examples are shown
-below. The first example shows how to compile OpenMP parallel code
-"vect-add.c" for host only:
-
- $ icc -xhost -no-offload -fopenmp vect-add.c -o vect-add-host
-
-To run this code on host, use:
-
- $ ./vect-add-host
-
-The second example shows how to compile the same code for Intel Xeon
-Phi:
-
- $ icc -mmic -fopenmp vect-add.c -o vect-add-mic
-
-### Execution of the Program in Native Mode on Intel Xeon Phi
-
-The user access to the Intel Xeon Phi is through the SSH. Since user
-home directories are mounted using NFS on the accelerator, users do not
-have to copy binary files or libraries between the host and accelerator.
-Â
-
-Get the PATH of MIC enabled libraries for currently used Intel Compiler
-(here was icc/2015.3.187-GNU-5.1.0-2.25 used) :
-
- $ echo $MIC_LD_LIBRARY_PATH
- /apps/all/icc/2015.3.187-GNU-5.1.0-2.25/composer_xe_2015.3.187/compiler/lib/mic
-
-To connect to the accelerator run:
-
- $ ssh mic0
-
-If the code is sequential, it can be executed directly:
-
- mic0 $ ~/path_to_binary/vect-add-seq-mic
-
-If the code is parallelized using OpenMP a set of additional libraries
-is required for execution. To locate these libraries new path has to be
-added to the LD_LIBRARY_PATH environment variable prior to the
-execution:
-
- mic0 $ export LD_LIBRARY_PATH=/apps/all/icc/2015.3.187-GNU-5.1.0-2.25/composer_xe_2015.3.187/compiler/lib/mic:$LD_LIBRARY_PATH
-
-Please note that the path exported in the previous example contains path
-to a specific compiler (here the version is 2015.3.187-GNU-5.1.0-2.25).
-This version number has to match with the version number of the Intel
-compiler module that was used to compile the code on the host computer.
-
-For your information the list of libraries and their location required
-for execution of an OpenMP parallel code on Intel Xeon Phi is:
-
-/apps/all/icc/2015.3.187-GNU-5.1.0-2.25/composer_xe_2015.3.187/compiler/lib/mic
-
-libiomp5.so
-libimf.so
-libsvml.so
-libirng.so
-libintlc.so.5
-
-Finally, to run the compiled code use:
-
- $ ~/path_to_binary/vect-add-mic
-
-OpenCL
--------------------
-
-OpenCL (Open Computing Language) is an open standard for
-general-purpose parallel programming for diverse mix of multi-core CPUs,
-GPU coprocessors, and other parallel processors. OpenCL provides a
-flexible execution model and uniform programming environment for
-software developers to write portable code for systems running on both
-the CPU and graphics processors or accelerators like the Intel® Xeon
-Phi.
-
-On Anselm OpenCL is installed only on compute nodes with MIC
-accelerator, therefore OpenCL code can be compiled only on these nodes.
-
- module load opencl-sdk opencl-rt
-
-Always load "opencl-sdk" (providing devel files like headers) and
-"opencl-rt" (providing dynamic library libOpenCL.so) modules to compile
-and link OpenCL code. Load "opencl-rt" for running your compiled code.
-
-There are two basic examples of OpenCL code in the following
-directory:
-
- /apps/intel/opencl-examples/
-
-First example "CapsBasic" detects OpenCL compatible hardware, here
-CPU and MIC, and prints basic information about the capabilities of
-it.Â
-
- /apps/intel/opencl-examples/CapsBasic/capsbasic
-
-To compile and run the example copy it to your home directory, get
-a PBS interactive session on of the nodes with MIC and run make for
-compilation. Make files are very basic and shows how the OpenCL code can
-be compiled on Anselm.
-
- $ cp /apps/intel/opencl-examples/CapsBasic/* .
- $ qsub -I -q qprod -l select=1:ncpus=24:accelerator=True:naccelerators=2:accelerator_model=phi7120 -A NONE-0-0
- $ make
-
-The compilation command for this example is:
-
- $ g++ capsbasic.cpp -lOpenCL -o capsbasic -I/apps/intel/opencl/include/
-
-After executing the complied binary file, following output should
-be displayed.
-
- ./capsbasic
-
- Number of available platforms: 1
- Platform names:
- Â Â Â [0] Intel(R) OpenCL [Selected]
- Number of devices available for each type:
- Â Â Â CL_DEVICE_TYPE_CPU: 1
- Â Â Â CL_DEVICE_TYPE_GPU: 0
- Â Â Â CL_DEVICE_TYPE_ACCELERATOR: 1
-
- ** Detailed information for each device ***
-
- CL_DEVICE_TYPE_CPU[0]
- Â Â Â CL_DEVICE_NAME:Â Â Â Â Â Â Â Intel(R) Xeon(R) CPU E5-2470 0 @ 2.30GHz
- Â Â Â CL_DEVICE_AVAILABLE: 1
-
- ...
-
- CL_DEVICE_TYPE_ACCELERATOR[0]
- Â Â Â CL_DEVICE_NAME: Intel(R) Many Integrated Core Acceleration Card
- Â Â Â CL_DEVICE_AVAILABLE: 1
-
- ...
-
-More information about this example can be found on Intel website:
-<http://software.intel.com/en-us/vcsource/samples/caps-basic/>
-
-The second example that can be found in
-"/apps/intel/opencl-examples" >directory is General Matrix
-Multiply. You can follow the the same procedure to download the example
-to your directory and compile it.Â
-
- $ cp -r /apps/intel/opencl-examples/* .
- $ qsub -I -q qprod -l select=1:ncpus=24:accelerator=True:naccelerators=2:accelerator_model=phi7120 -A NONE-0-0
- $ cd GEMM
- $ make
-
-The compilation command for this example is:
-
- $ g++ cmdoptions.cpp gemm.cpp ../common/basic.cpp ../common/cmdparser.cpp ../common/oclobject.cpp -I../common -lOpenCL -o gemm -I/apps/intel/opencl/include/
-
-To see the performance of Intel Xeon Phi performing the DGEMM run
-the example as follows:
-
- ./gemm -d 1
- Platforms (1):
- [0] Intel(R) OpenCL [Selected]
- Devices (2):
- [0] Intel(R) Xeon(R) CPU E5-2470 0 @ 2.30GHz
- [1] Intel(R) Many Integrated Core Acceleration Card [Selected]
- Build program options: "-DT=float -DTILE_SIZE_M=1 -DTILE_GROUP_M=16 -DTILE_SIZE_N=128 -DTILE_GROUP_N=1 -DTILE_SIZE_K=8"
- Running gemm_nn kernel with matrix size: 3968x3968
- Memory row stride to ensure necessary alignment: 15872 bytes
- Size of memory region for one matrix: 62980096 bytes
- Using alpha = 0.57599 and beta = 0.872412
- ...
- Host time: 0.292953 sec.
- Host perf: 426.635 GFLOPS
- Host time: 0.293334 sec.
- Host perf: 426.081 GFLOPS
- ...
-
-Please note: GNU compiler is used to compile the OpenCL codes for
-Intel MIC. You do not need to load Intel compiler module.
-
-MPI
-----------------
-
-### Environment setup and compilation
-
-To achieve best MPI performance always use following setup for Intel MPI
-on Xeon Phi accelerated nodes:
-
- $ export I_MPI_FABRICS=shm:dapl
- $ export I_MPI_DAPL_PROVIDER_LIST=ofa-v2-mlx4_0-1u,ofa-v2-scif0,ofa-v2-mcm-1
-
-This ensures, that MPI inside node will use SHMEM communication, between
-HOST and Phi the IB SCIF will be used and between different nodes or
-Phi's on diferent nodes a CCL-Direct proxy will be used.
-
-Please note: Other FABRICS like tcp,ofa may be used (even combined with
-shm) but there's severe loss of performance (by order of magnitude).
-Usage of single DAPL PROVIDER (e. g.
-I_MPI_DAPL_PROVIDER=ofa-v2-mlx4_0-1u) will cause failure of
-Host<->Phi and/or Phi<->Phi communication.
-Usage of the I_MPI_DAPL_PROVIDER_LIST on non-accelerated node will
-cause failure of any MPI communication, since those nodes don't have
-SCIF device and there's no CCL-Direct proxy runnig.
-
-Again an MPI code for Intel Xeon Phi has to be compiled on a compute
-node with accelerator and MPSS software stack installed. To get to a
-compute node with accelerator use:
-
- $ qsub -I -q qprod -l select=1:ncpus=24:accelerator=True:naccelerators=2:accelerator_model=phi7120 -A NONE-0-0
-
-The only supported implementation of MPI standard for Intel Xeon Phi is
-Intel MPI. To setup a fully functional development environment a
-combination of Intel compiler and Intel MPI has to be used. On a host
-load following modules before compilation:
-
- $ module load intel impi
-
-To compile an MPI code for host use:
-
- $ mpiicc -xhost -o mpi-test mpi-test.c
-
-To compile the same code for Intel Xeon Phi architecture use:
-
- $ mpiicc -mmic -o mpi-test-mic mpi-test.c
-
-Or, if you are using Fortran :
-
- $ mpiifort -mmic -o mpi-test-mic mpi-test.f90
-
-An example of basic MPI version of "hello-world" example in C language,
-that can be executed on both host and Xeon Phi is (can be directly copy
-and pasted to a .c file)
-
-`
-#include <stdio.h>
-#include <mpi.h>
-
-int main (argc, argv)
-Â Â Â Â int argc;
-Â Â Â Â char *argv[];
-{
-Â int rank, size;
-
-Â int len;
-Â char node[MPI_MAX_PROCESSOR_NAME];
-
-Â MPI_Init (&argc, &argv);Â Â Â Â Â /* starts MPI */
-Â MPI_Comm_rank (MPI_COMM_WORLD, &rank);Â Â Â Â Â Â Â /* get current process id */
-Â MPI_Comm_size (MPI_COMM_WORLD, &size);Â Â Â Â Â Â Â /* get number of processes */
-
-Â MPI_Get_processor_name(node,&len);
-
-Â printf( "Hello world from process %d of %d on host %s n", rank, size, node );
-Â MPI_Finalize();
-Â return 0;
-}
-`
-
-### MPI programming models
-
-Intel MPI for the Xeon Phi coprocessors offers different MPI
-programming models:
-
-Host-only model** - all MPI ranks reside on the host. The coprocessors
-can be used by using offload pragmas. (Using MPI calls inside offloaded
-code is not supported.)**
-
-Coprocessor-only model** - all MPI ranks reside only on the
-coprocessors.
-
-Symmetric model** - the MPI ranks reside on both the host and the
-coprocessor. Most general MPI case.
-
-###Host-only model
-
-In this case all environment variables are set by modules,
-so to execute the compiled MPI program on a single node, use:
-
- $ mpirun -np 4 ./mpi-test
-
-The output should be similar to:
-
- Hello world from process 1 of 4 on host r38u31n1000
- Hello world from process 3 of 4 on host r38u31n1000
- Hello world from process 2 of 4 on host r38u31n1000
- Hello world from process 0 of 4 on host r38u31n1000
-
-### Coprocessor-only model
-
-There are two ways how to execute an MPI code on a single
-coprocessor: 1.) lunch the program using "**mpirun**" from the
-coprocessor; or 2.) lunch the task using "**mpiexec.hydra**" from a
-host.
-
-Execution on coprocessor**Â
-
-Similarly to execution of OpenMP programs in native mode, since the
-environmental module are not supported on MIC, user has to setup paths
-to Intel MPI libraries and binaries manually. One time setup can be done
-by creating a "**.profile**" file in user's home directory. This file
-sets up the environment on the MIC automatically once user access to the
-accelerator through the SSH.
-
-At first get the LD_LIBRARY_PATH for currenty used Intel Compiler and
-Intel MPI:
-
- $ echo $MIC_LD_LIBRARY_PATH
- /apps/all/imkl/11.2.3.187-iimpi-7.3.5-GNU-5.1.0-2.25/mkl/lib/mic:/apps/all/imkl/11.2.3.187-iimpi-7.3.5-GNU-5.1.0-2.25/lib/mic:/apps/all/icc/2015.3.187-GNU-5.1.0-2.25/composer_xe_2015.3.187/compiler/lib/mic/
-
-Use it in your ~/.profile:
-
- $ vim ~/.profile
-
- PS1='[u@h W]$ '
- export PATH=/usr/bin:/usr/sbin:/bin:/sbin
-
- #IMPI
- export PATH=/apps/all/impi/5.0.3.048-iccifort-2015.3.187-GNU-5.1.0-2.25/mic/bin/:$PATH
-
- #OpenMP (ICC, IFORT), IMKL and IMPI
- export LD_LIBRARY_PATH=/apps/all/imkl/11.2.3.187-iimpi-7.3.5-GNU-5.1.0-2.25/mkl/lib/mic:/apps/all/imkl/11.2.3.187-iimpi-7.3.5-GNU-5.1.0-2.25/lib/mic:/apps/all/icc/2015.3.187-GNU-5.1.0-2.25/composer_xe_2015.3.187/compiler/lib/mic:$LD_LIBRARY_PATH
-
-Please note:
-Â - this file sets up both environmental variable for both MPI and OpenMP
-libraries.
-Â - this file sets up the paths to a particular version of Intel MPI
-library and particular version of an Intel compiler. These versions have
-to match with loaded modules.
-
-To access a MIC accelerator located on a node that user is currently
-connected to, use:
-
- $ ssh mic0
-
-or in case you need specify a MIC accelerator on a particular node, use:
-
- $ ssh r38u31n1000-mic0
-
-To run the MPI code in parallel on multiple core of the accelerator,
-use:
-
- $ mpirun -np 4 ./mpi-test-mic
-
-The output should be similar to:
-
- Hello world from process 1 of 4 on host r38u31n1000-mic0
- Hello world from process 2 of 4 on host r38u31n1000-mic0
- Hello world from process 3 of 4 on host r38u31n1000-mic0
- Hello world from process 0 of 4 on host r38u31n1000-mic0
-
-Â **
-
-**Execution on host**
-
-If the MPI program is launched from host instead of the coprocessor, the
-environmental variables are not set using the ".profile" file. Therefore
-user has to specify library paths from the command line when calling
-"mpiexec".
-
-First step is to tell mpiexec that the MPI should be executed on a local
-accelerator by setting up the environmental variable "I_MPI_MIC"
-
- $ export I_MPI_MIC=1
-
-Now the MPI program can be executed as:
-
- $ mpirun -genv LD_LIBRARY_PATH $MIC_LD_LIBRARY_PATH -host mic0 -n 4 ~/mpi-test-mic
-
-or using mpirun
-
- $ mpirun -genv LD_LIBRARY_PATH $MIC_LD_LIBRARY_PATH -host mic0 -n 4 ~/mpi-test-mic
-
-Please note:
-Â - the full path to the binary has to specified (here:
-"**>~/mpi-test-mic**")
-Â - the LD_LIBRARY_PATH has to match with Intel MPI module used to
-compile the MPI code
-
-The output should be again similar to:
-
- Hello world from process 1 of 4 on host r38u31n1000-mic0
- Hello world from process 2 of 4 on host r38u31n1000-mic0
- Hello world from process 3 of 4 on host r38u31n1000-mic0
- Hello world from process 0 of 4 on host r38u31n1000-mic0
-
-Please note that the "mpiexec.hydra" requires a file
-"**>pmi_proxy**" from Intel MPI library to be copied to the
-MIC filesystem. If the file is missing please contact the system
-administrators. A simple test to see if the file is present is to
-execute:
-
- Â Â $ ssh mic0 ls /bin/pmi_proxy
- Â /bin/pmi_proxy
-
-Â **
-
-**Execution on host - MPI processes distributed over multiple
-accelerators on multiple nodes**
-
-To get access to multiple nodes with MIC accelerator, user has to
-use PBS to allocate the resources. To start interactive session, that
-allocates 2 compute nodes = 2 MIC accelerators run qsub command with
-following parameters:
-
- $ qsub -I -q qprod -l select=2:ncpus=24:accelerator=True:naccelerators=2:accelerator_model=phi7120 -A NONE-0-0
-
- $ module load intel impi
-
-This command connects user through ssh to one of the nodes
-immediately. To see the other nodes that have been allocated use:
-
- $ cat $PBS_NODEFILE
-
-For example:
-
- r38u31n1000.bullx
- r38u32n1001.bullx
-
-This output means that the PBS allocated nodes r38u31n1000 and
-r38u32n1001, which means that user has direct access to
-"**r38u31n1000-mic0**" and "**>r38u32n1001-mic0**"
-accelerators.
-
-Please note: At this point user can connect to any of the
-allocated nodes or any of the allocated MIC accelerators using ssh:
-- to connect to the second node : ** $
-ssh >r38u32n1001**
-- to connect to the accelerator on the first node from the first
-node: **$ ssh
-r38u31n1000-mic0** or **
-$ ssh mic0**
--** to connect to the accelerator on the second node from the first
-node: **$ ssh
-r38u32n1001-mic0**
-
-At this point we expect that correct modules are loaded and binary
-is compiled. For parallel execution the mpiexec.hydra is used.
-Again the first step is to tell mpiexec that the MPI can be executed on
-MIC accelerators by setting up the environmental variable "I_MPI_MIC",
-don't forget to have correct FABRIC and PROVIDER defined.
-
- $ export I_MPI_MIC=1
- $ export I_MPI_FABRICS=shm:dapl
- $ export I_MPI_DAPL_PROVIDER_LIST=ofa-v2-mlx4_0-1u,ofa-v2-scif0,ofa-v2-mcm-1
-
-The launch the MPI program use:
-
- $ mpirun -genv LD_LIBRARY_PATH $MIC_LD_LIBRARY_PATH
- -host r38u31n1000-mic0 -n 4 ~/mpi-test-mic
- : -host r38u32n1001-mic0 -n 6 ~/mpi-test-mic
-
-or using mpirun:
-
- $ mpirun -genv LD_LIBRARY_PATH
- -host r38u31n1000-mic0 -n 4 ~/mpi-test-mic
- : -host r38u32n1001-mic0 -n 6 ~/mpi-test-mic
-
-In this case four MPI processes are executed on accelerator
-r38u31n1000-mic and six processes are executed on accelerator
-r38u32n1001-mic0. The sample output (sorted after execution) is:
-
- Hello world from process 0 of 10 on host r38u31n1000-mic0
- Hello world from process 1 of 10 on host r38u31n1000-mic0
- Hello world from process 2 of 10 on host r38u31n1000-mic0
- Hello world from process 3 of 10 on host r38u31n1000-mic0
- Hello world from process 4 of 10 on host r38u32n1001-mic0
- Hello world from process 5 of 10 on host r38u32n1001-mic0
- Hello world from process 6 of 10 on host r38u32n1001-mic0
- Hello world from process 7 of 10 on host r38u32n1001-mic0
- Hello world from process 8 of 10 on host r38u32n1001-mic0
- Hello world from process 9 of 10 on host r38u32n1001-mic0
-
-The same way MPI program can be executed on multiple hosts:Â
-
- $ mpirun -genv LD_LIBRARY_PATH $MIC_LD_LIBRARY_PATH
- -host r38u31n1000 -n 4 ~/mpi-test
- : -host r38u32n1001 -n 6 ~/mpi-test
-
-###Symmetric model
-
-In a symmetric mode MPI programs are executed on both host
-computer(s) and MIC accelerator(s). Since MIC has a different
-architecture and requires different binary file produced by the Intel
-compiler two different files has to be compiled before MPI program is
-executed.
-
-In the previous section we have compiled two binary files, one for
-hosts "**mpi-test**" and one for MIC accelerators "**mpi-test-mic**".
-These two binaries can be executed at once using mpiexec.hydra:
-
- $ mpirun
- -genv $MIC_LD_LIBRARY_PATH
- -host r38u32n1001 -n 2 ~/mpi-test
- : -host r38u32n1001-mic0 -n 2 ~/mpi-test-mic
-
-In this example the first two parameters (line 2 and 3) sets up required
-environment variables for execution. The third line specifies binary
-that is executed on host (here r38u32n1001) and the last line specifies
-the binary that is execute on the accelerator (here r38u32n1001-mic0).
-
-The output of the program is:
-
- Hello world from process 0 of 4 on host r38u32n1001
- Hello world from process 1 of 4 on host r38u32n1001
- Hello world from process 2 of 4 on host r38u32n1001-mic0
- Hello world from process 3 of 4 on host r38u32n1001-mic0
-
-The execution procedure can be simplified by using the mpirun
-command with the machine file a a parameter. Machine file contains list
-of all nodes and accelerators that should used to execute MPI processes.
-
-An example of a machine file that uses 2 >hosts (r38u32n1001
-and r38u33n1002) and 2 accelerators **(r38u32n1001-mic0** and
-r38u33n1002-mic0**) to run 2 MPI processes
-on each of them:
-
- $ cat hosts_file_mix
- r38u32n1001:2
- r38u32n1001-mic0:2
- r38u33n1002:2
- r38u33n1002-mic0:2
-
-In addition if a naming convention is set in a way that the name
-of the binary for host is **"bin_name"**Â and the name of the binary
-for the accelerator is **"bin_name-mic"** then by setting up the
-environment variable **I_MPI_MIC_POSTFIX** to **"-mic"** user do not
-have to specify the names of booth binaries. In this case mpirun needs
-just the name of the host binary file (i.e. "mpi-test") and uses the
-suffix to get a name of the binary for accelerator (i..e.
-"mpi-test-mic").
-
- $ export I_MPI_MIC_POSTFIX=-mic
-
-Â >To run the MPI code using mpirun and the machine file
-"hosts_file_mix" use:
-
- $ mpirun
- -genv LD_LIBRARY_PATH $MIC_LD_LIBRARY_PATH
- -machinefile hosts_file_mix
- ~/mpi-test
-
-A possible output of the MPI "hello-world" example executed on two
-hosts and two accelerators is:
-
- Hello world from process 0 of 8 on host r38u31n1000
- Hello world from process 1 of 8 on host r38u31n1000
- Hello world from process 2 of 8 on host r38u31n1000-mic0
- Hello world from process 3 of 8 on host r38u31n1000-mic0
- Hello world from process 4 of 8 on host r38u32n1001
- Hello world from process 5 of 8 on host r38u32n1001
- Hello world from process 6 of 8 on host r38u32n1001-mic0
- Hello world from process 7 of 8 on host r38u32n1001-mic0
-
-Using the PBS automatically generated node-files
-
-PBS also generates a set of node-files that can be used instead of
-manually creating a new one every time. Three node-files are genereated:
-
-**Host only node-file:**
-Â - /lscratch/${PBS_JOBID}/nodefile-cn
-MIC only node-file:
-Â - /lscratch/${PBS_JOBID}/nodefile-mic
-Host and MIC node-file:
-Â - /lscratch/${PBS_JOBID}/nodefile-mix
-
-Please note each host or accelerator is listed only per files. User has
-to specify how many jobs should be executed per node using "-n"
-parameter of the mpirun command.
-
-Optimization
-------------
-
-For more details about optimization techniques please read Intel
-document [Optimization and Performance Tuning for Intel® Xeon Phi™
-Coprocessors](http://software.intel.com/en-us/articles/optimization-and-performance-tuning-for-intel-xeon-phi-coprocessors-part-1-optimization "http://software.intel.com/en-us/articles/optimization-and-performance-tuning-for-intel-xeon-phi-coprocessors-part-1-optimization")
-
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-Java
-====
-
-Java on the cluster
-
-
-
-Java is available on the cluster. Activate java by loading the Java
-module
-
- $ module load Java
-
-Note that the Java module must be loaded on the compute nodes as well,
-in order to run java on compute nodes.
-
-Check for java version and path
-
- $ java -version
- $ which java
-
-With the module loaded, not only the runtime environment (JRE), but also
-the development environment (JDK) with the compiler is available.
-
- $ javac -version
- $ which javac
-
-Java applications may use MPI for interprocess communication, in
-conjunction with OpenMPI. Read more
-on <http://www.open-mpi.org/faq/?category=java>.
-This functionality is currently not supported on Anselm cluster. In case
-you require the java interface to MPI, please contact [cluster
-support](https://support.it4i.cz/rt/).
-
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-Running OpenMPI
-===============
-
-
-
-OpenMPI program execution
--------------------------
-
-The OpenMPI programs may be executed only via the PBS Workload manager,
-by entering an appropriate queue. On the cluster, the **OpenMPI 1.8.6**
-is OpenMPI based MPI implementation.
-
-### Basic usage
-
-Use the mpiexec to run the OpenMPI code.
-
-Example:
-
- $ qsub -q qexp -l select=4:ncpus=24 -I
- qsub: waiting for job 15210.isrv5 to start
- qsub: job 15210.isrv5 ready
-
- $ pwd
- /home/username
-
- $ module load OpenMPI
- $ mpiexec -pernode ./helloworld_mpi.x
- Hello world! from rank 0 of 4 on host r1i0n17
- Hello world! from rank 1 of 4 on host r1i0n5
- Hello world! from rank 2 of 4 on host r1i0n6
- Hello world! from rank 3 of 4 on host r1i0n7
-
-Please be aware, that in this example, the directive **-pernode** is
-used to run only **one task per node**, which is normally an unwanted
-behaviour (unless you want to run hybrid code with just one MPI and 24
-OpenMP tasks per node). In normal MPI programs **omit the -pernode
-directive** to run up to 24 MPI tasks per each node.
-
-In this example, we allocate 4 nodes via the express queue
-interactively. We set up the openmpi environment and interactively run
-the helloworld_mpi.x program.
-Note that the executable
-helloworld_mpi.x must be available within the
-same path on all nodes. This is automatically fulfilled on the /home and
-/scratch filesystem.
-
-You need to preload the executable, if running on the local ramdisk /tmp
-filesystem
-
- $ pwd
- /tmp/pbs.15210.isrv5
-
- $ mpiexec -pernode --preload-binary ./helloworld_mpi.x
- Hello world! from rank 0 of 4 on host r1i0n17
- Hello world! from rank 1 of 4 on host r1i0n5
- Hello world! from rank 2 of 4 on host r1i0n6
- Hello world! from rank 3 of 4 on host r1i0n7
-
-In this example, we assume the executable
-helloworld_mpi.x is present on compute node
-r1i0n17 on ramdisk. We call the mpiexec whith the **--preload-binary**
-argument (valid for openmpi). The mpiexec will copy the executable from
-r1i0n17 to the /tmp/pbs.15210.isrv5
-directory on r1i0n5, r1i0n6 and r1i0n7 and execute the program.
-
-MPI process mapping may be controlled by PBS parameters.
-
-The mpiprocs and ompthreads parameters allow for selection of number of
-running MPI processes per node as well as number of OpenMP threads per
-MPI process.
-
-### One MPI process per node
-
-Follow this example to run one MPI process per node, 24 threads per
-process.Â
-
- $ qsub -q qexp -l select=4:ncpus=24:mpiprocs=1:ompthreads=24 -I
-
- $ module load OpenMPI
-
- $ mpiexec --bind-to-none ./helloworld_mpi.x
-
-In this example, we demonstrate recommended way to run an MPI
-application, using 1 MPI processes per node and 24 threads per socket,
-on 4 nodes.
-
-### Two MPI processes per node
-
-Follow this example to run two MPI processes per node, 8 threads per
-process. Note the options to mpiexec.
-
- $ qsub -q qexp -l select=4:ncpus=24:mpiprocs=2:ompthreads=12 -I
-
- $ module load OpenMPI
-
- $ mpiexec -bysocket -bind-to-socket ./helloworld_mpi.x
-
-In this example, we demonstrate recommended way to run an MPI
-application, using 2 MPI processes per node and 12 threads per socket,
-each process and its threads bound to a separate processor socket of the
-node, on 4 nodes
-
-### 24 MPI processes per node
-
-Follow this example to run 24 MPI processes per node, 1 thread per
-process. Note the options to mpiexec.
-
- $ qsub -q qexp -l select=4:ncpus=24:mpiprocs=24:ompthreads=1 -I
-
- $ module load OpenMPI
-
- $ mpiexec -bycore -bind-to-core ./helloworld_mpi.x
-
-In this example, we demonstrate recommended way to run an MPI
-application, using 24 MPI processes per node, single threaded. Each
-process is bound to separate processor core, on 4 nodes.
-
-### OpenMP thread affinity
-
-Important! Bind every OpenMP thread to a core!
-
-In the previous two examples with one or two MPI processes per node, the
-operating system might still migrate OpenMP threads between cores. You
-might want to avoid this by setting these environment variable for GCC
-OpenMP:
-
- $ export GOMP_CPU_AFFINITY="0-23"
-
-or this one for Intel OpenMP:
-
- $ export KMP_AFFINITY=granularity=fine,compact,1,0
-
-As of OpenMP 4.0 (supported by GCC 4.9 and later and Intel 14.0 and
-later) the following variables may be used for Intel or GCC:
-
- $ export OMP_PROC_BIND=true
- $ export OMP_PLACES=coresÂ
-
-OpenMPI Process Mapping and Binding
-------------------------------------------------
-
-The mpiexec allows for precise selection of how the MPI processes will
-be mapped to the computational nodes and how these processes will bind
-to particular processor sockets and cores.
-
-MPI process mapping may be specified by a hostfile or rankfile input to
-the mpiexec program. Altough all implementations of MPI provide means
-for process mapping and binding, following examples are valid for the
-openmpi only.
-
-### Hostfile
-
-Example hostfile
-
- r1i0n17.smc.salomon.it4i.cz
- r1i0n5.smc.salomon.it4i.cz
- r1i0n6.smc.salomon.it4i.cz
- r1i0n7.smc.salomon.it4i.cz
-
-Use the hostfile to control process placement
-
- $ mpiexec -hostfile hostfile ./helloworld_mpi.x
- Hello world! from rank 0 of 4 on host r1i0n17
- Hello world! from rank 1 of 4 on host r1i0n5
- Hello world! from rank 2 of 4 on host r1i0n6
- Hello world! from rank 3 of 4 on host r1i0n7
-
-In this example, we see that ranks have been mapped on nodes according
-to the order in which nodes show in the hostfile
-
-### Rankfile
-
-Exact control of MPI process placement and resource binding is provided
-by specifying a rankfile
-
-Appropriate binding may boost performance of your application.
-
-Example rankfile
-
- rank 0=r1i0n7.smc.salomon.it4i.cz slot=1:0,1
- rank 1=r1i0n6.smc.salomon.it4i.cz slot=0:*
- rank 2=r1i0n5.smc.salomon.it4i.cz slot=1:1-2
- rank 3=r1i0n17.smc.salomon slot=0:1,1:0-2
- rank 4=r1i0n6.smc.salomon.it4i.cz slot=0:*,1:*
-
-This rankfile assumes 5 ranks will be running on 4 nodes and provides
-exact mapping and binding of the processes to the processor sockets and
-cores
-
-Explanation:
-rank 0 will be bounded to r1i0n7, socket1 core0 and core1
-rank 1 will be bounded to r1i0n6, socket0, all cores
-rank 2 will be bounded to r1i0n5, socket1, core1 and core2
-rank 3 will be bounded to r1i0n17, socket0 core1, socket1 core0, core1,
-core2
-rank 4 will be bounded to r1i0n6, all cores on both sockets
-
- $ mpiexec -n 5 -rf rankfile --report-bindings ./helloworld_mpi.x
- [r1i0n17:11180] MCW rank 3 bound to socket 0[core 1] socket 1[core 0-2]: [. B . . . . . . . . . .][B B B . . . . . . . . .] (slot list 0:1,1:0-2)
- [r1i0n7:09928] MCW rank 0 bound to socket 1[core 0-1]: [. . . . . . . . . . . .][B B . . . . . . . . . .] (slot list 1:0,1)
- [r1i0n6:10395] MCW rank 1 bound to socket 0[core 0-7]: [B B B B B B B B B B B B][. . . . . . . . . . . .] (slot list 0:*)
- [r1i0n5:10406] MCW rank 2 bound to socket 1[core 1-2]: [. . . . . . . . . . . .][. B B . . . . . . . . .] (slot list 1:1-2)
- [r1i0n6:10406] MCW rank 4 bound to socket 0[core 0-7] socket 1[core 0-7]: [B B B B B B B B B B B B][B B B B B B B B B B B B] (slot list 0:*,1:*)
- Hello world! from rank 3 of 5 on host r1i0n17
- Hello world! from rank 1 of 5 on host r1i0n6
- Hello world! from rank 0 of 5 on host r1i0n7
- Hello world! from rank 4 of 5 on host r1i0n6
- Hello world! from rank 2 of 5 on host r1i0n5
-
-In this example we run 5 MPI processes (5 ranks) on four nodes. The
-rankfile defines how the processes will be mapped on the nodes, sockets
-and cores. The **--report-bindings** option was used to print out the
-actual process location and bindings. Note that ranks 1 and 4 run on the
-same node and their core binding overlaps.
-
-It is users responsibility to provide correct number of ranks, sockets
-and cores.
-
-### Bindings verification
-
-In all cases, binding and threading may be verified by executing for
-example:
-
- $ mpiexec -bysocket -bind-to-socket --report-bindings echo
- $ mpiexec -bysocket -bind-to-socket numactl --show
- $ mpiexec -bysocket -bind-to-socket echo $OMP_NUM_THREADS
-
-Changes in OpenMPI 1.8
-----------------------
-
-Some options have changed in OpenMPI version 1.8.
-
- |version 1.6.5 |version 1.8.1 |
- | --- | --- |
- |--bind-to-none |--bind-to none |
- |--bind-to-core |--bind-to core |
- |--bind-to-socket |--bind-to socket |
- |-bysocket |--map-by socket |
- |-bycore |--map-by core |
- |-pernode |--map-by ppr:1:node\ |
-
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-MPI
-===
-
-
-
-Setting up MPI Environment
---------------------------
-
-The Salomon cluster provides several implementations of the MPI library:
-
- -------------------------------------------------------------------------
- MPI Library Thread support
- ------ |---|---|---
- **Intel MPI 4.1** Full thread support up to
- MPI_THREAD_MULTIPLE
-
- **Intel MPI 5.0** Full thread support up to
- MPI_THREAD_MULTIPLE
-
- OpenMPI 1.8.6 Full thread support up to
- MPI_THREAD_MULTIPLE, MPI-3.0
- support
-
- SGI MPT 2.12
- -------------------------------------------------------------------------
-
-MPI libraries are activated via the environment modules.
-
-Look up section modulefiles/mpi in module avail
-
- $ module avail
- ------------------------------ /apps/modules/mpi -------------------------------
- impi/4.1.1.036-iccifort-2013.5.192
- impi/4.1.1.036-iccifort-2013.5.192-GCC-4.8.3
- impi/5.0.3.048-iccifort-2015.3.187
- impi/5.0.3.048-iccifort-2015.3.187-GNU-5.1.0-2.25
- MPT/2.12
- OpenMPI/1.8.6-GNU-5.1.0-2.25
-
-There are default compilers associated with any particular MPI
-implementation. The defaults may be changed, the MPI libraries may be
-used in conjunction with any compiler.
-The defaults are selected via the modules in following way
-
- --------------------------------------------------------------------------
- Module MPI Compiler suite
- ------------------ |---|---|-------------- ------------------------
- impi-5.0.3.048-iccifort- Intel MPI 5.0.3
- 2015.3.187
-
- OpenMP-1.8.6-GNU-5.1.0-2 OpenMPI 1.8.6
- .25
- --------------------------------------------------------------------------
-
-Examples:
-
- $ module load gompi/2015b
-
-In this example, we activate the latest OpenMPI with latest GNU
-compilers (OpenMPI 1.8.6 and GCC 5.1). Please see more information about
-toolchains in section [Environment and
-Modules](../../environment-and-modules.html)Â .
-
-To use OpenMPI with the intel compiler suite, use
-
- $ module load iompi/2015.03
-
-In this example, the openmpi 1.8.6 using intel compilers is activated.
-It's used "iompi" toolchain.
-
-Compiling MPI Programs
-----------------------
-
-After setting up your MPI environment, compile your program using one of
-the mpi wrappers
-
- $ mpicc -v
- $ mpif77 -v
- $ mpif90 -v
-
-When using Intel MPI, use the following MPI wrappers:
-
- $ mpicc
- $ mpiifortÂ
-
-Wrappers mpif90, mpif77 that are provided by Intel MPI are designed for
-gcc and gfortran. You might be able to compile MPI code by them even
-with Intel compilers, but you might run into problems (for example,
-native MIC compilation with -mmic does not work with mpif90).
-
-Example program:
-
- // helloworld_mpi.c
- #include <stdio.h>
-
- #include<mpi.h>
-
- int main(int argc, char **argv) {
-
- int len;
- int rank, size;
- char node[MPI_MAX_PROCESSOR_NAME];
-
- // Initiate MPI
- MPI_Init(&argc, &argv);
- MPI_Comm_rank(MPI_COMM_WORLD,&rank);
- MPI_Comm_size(MPI_COMM_WORLD,&size);
-
- // Get hostame and print
- MPI_Get_processor_name(node,&len);
- printf("Hello world! from rank %d of %d on host %sn",rank,size,node);
-
- // Finalize and exit
- MPI_Finalize();
-
- return 0;
- }
-
-Compile the above example with
-
- $ mpicc helloworld_mpi.c -o helloworld_mpi.x
-
-Running MPI Programs
---------------------
-
-The MPI program executable must be compatible with the loaded MPI
-module.
-Always compile and execute using the very same MPI module.
-
-It is strongly discouraged to mix mpi implementations. Linking an
-application with one MPI implementation and running mpirun/mpiexec form
-other implementation may result in unexpected errors.
-
-The MPI program executable must be available within the same path on all
-nodes. This is automatically fulfilled on the /home and /scratch
-filesystem. You need to preload the executable, if running on the local
-scratch /lscratch filesystem.
-
-### Ways to run MPI programs
-
-Optimal way to run an MPI program depends on its memory requirements,
-memory access pattern and communication pattern.
-
-Consider these ways to run an MPI program:
-1. One MPI process per node, 24 threads per process
-2. Two MPI processes per node, 12 threads per process
-3. 24 MPI processes per node, 1 thread per process.
-
-One MPI** process per node, using 24 threads, is most useful for
-memory demanding applications, that make good use of processor cache
-memory and are not memory bound. This is also a preferred way for
-communication intensive applications as one process per node enjoys full
-bandwidth access to the network interface.Â
-
-Two MPI** processes per node, using 12 threads each, bound to
-processor socket is most useful for memory bandwidth bound applications
-such as BLAS1 or FFT, with scalable memory demand. However, note that
-the two processes will share access to the network interface. The 12
-threads and socket binding should ensure maximum memory access bandwidth
-and minimize communication, migration and numa effect overheads.
-
-Important! Bind every OpenMP thread to a core!
-
-In the previous two cases with one or two MPI processes per node, the
-operating system might still migrate OpenMP threads between cores. You
-want to avoid this by setting the KMP_AFFINITY or GOMP_CPU_AFFINITY
-environment variables.
-
-**24 MPI** processes per node, using 1 thread each bound to processor
-core is most suitable for highly scalable applications with low
-communication demand.
-
-### Running OpenMPI
-
-The [**OpenMPI 1.8.6**](http://www.open-mpi.org/) is
-based on OpenMPI. Read more on [how to run
-OpenMPI](Running_OpenMPI.html) based MPI.
-
-Â
-
-The Intel MPI may run on the[Intel Xeon
-Ph](../intel-xeon-phi.html)i accelerators as well. Read
-more on [how to run Intel MPI on
-accelerators](../intel-xeon-phi.html).
-
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-MPI4Py (MPI for Python)
-=======================
-
-OpenMPI interface to Python
-
-
-
-Introduction
-------------
-
-MPI for Python provides bindings of the Message Passing Interface (MPI)
-standard for the Python programming language, allowing any Python
-program to exploit multiple processors.
-
-This package is constructed on top of the MPI-1/2 specifications and
-provides an object oriented interface which closely follows MPI-2 C++
-bindings. It supports point-to-point (sends, receives) and collective
-(broadcasts, scatters, gathers) communications of any picklable Python
-object, as well as optimized communications of Python object exposing
-the single-segment buffer interface (NumPy arrays, builtin
-bytes/string/array objects).
-
-On Anselm MPI4Py is available in standard Python modules.
-
-Modules
--------
-
-MPI4Py is build for OpenMPI. Before you start with MPI4Py you need to
-load Python and OpenMPI modules. You can use toolchain, that loads
-Python and OpenMPI at once.
-
- $ module load Python/2.7.9-foss-2015g
-
-Execution
----------
-
-You need to import MPI to your python program. Include the following
-line to the python script:
-
- from mpi4py import MPI
-
-The MPI4Py enabled python programs [execute as any other
-OpenMPI](Running_OpenMPI.html) code.The simpliest way is
-to run
-
- $ mpiexec python <script>.py
-
-For example
-
- $ mpiexec python hello_world.py
-
-Examples
---------
-
-### Hello world!
-
- from mpi4py import MPI
-
- comm = MPI.COMM_WORLD
-
- print "Hello! I'm rank %d from %d running in total..." % (comm.rank, comm.size)
-
- comm.Barrier() Â # wait for everybody to synchronize
-
-###Collective Communication with NumPy arrays
-
- from __future__ import division
- from mpi4py import MPI
- import numpy as np
-
- comm = MPI.COMM_WORLD
-
- print("-"*78)
- print(" Running on %d cores" % comm.size)
- print("-"*78)
-
- comm.Barrier()
-
- # Prepare a vector of N=5 elements to be broadcasted...
- N = 5
- if comm.rank == 0:
- Â Â A = np.arange(N, dtype=np.float64) Â Â # rank 0 has proper data
- else:
- Â Â A = np.empty(N, dtype=np.float64) Â Â # all other just an empty array
-
- # Broadcast A from rank 0 to everybody
- comm.Bcast( [A, MPI.DOUBLE] )
-
- # Everybody should now have the same...
- print "[%02d] %s" % (comm.rank, A)
-
-Execute the above code as:
-
- $ qsub -q qexp -l select=4:ncpus=24:mpiprocs=24:ompthreads=1 -I
-
- $ module load Python/2.7.9-foss-2015g
-
- $ mpiexec --map-by core --bind-to core python hello_world.py
-
-In this example, we run MPI4Py enabled code on 4 nodes, 24 cores per
-node (total of 96 processes), each python process is bound to a
-different core.
-More examples and documentation can be found on [MPI for Python
-webpage](https://pythonhosted.org/mpi4py/usrman/index.html).
-
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-Numerical languages
-===================
-
-Interpreted languages for numerical computations and analysis
-
-
-
-Introduction
-------------
-
-This section contains a collection of high-level interpreted languages,
-primarily intended for numerical computations.
-
-Matlab
-------
-
-MATLAB®^ is a high-level language and interactive environment for
-numerical computation, visualization, and programming.
-
- $ module load MATLAB
- $ matlab
-
-Read more at the [Matlab
-page](matlab.html).
-
-Octave
-------
-
-GNU Octave is a high-level interpreted language, primarily intended for
-numerical computations. The Octave language is quite similar to Matlab
-so that most programs are easily portable.
-
- $ module load Octave
- $ octave
-
-Read more at the [Octave page](octave.html).
-
-R
--
-
-The R is an interpreted language and environment for statistical
-computing and graphics.
-
- $ module load R
- $ R
-
-Read more at the [R page](r.html).
-
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-Matlab
-======
-
-
-
-Introduction
-------------
-
-Matlab is available in versions R2015a and R2015b. There are always two
-variants of the release:
-
-- Non commercial or so called EDU variant, which can be used for
- common research and educational purposes.
-- Commercial or so called COM variant, which can used also for
- commercial activities. The licenses for commercial variant are much
- more expensive, so usually the commercial variant has only subset of
- features compared to the EDU available.
-
-Â
-
-To load the latest version of Matlab load the module
-
- $ module load MATLAB
-
-By default the EDU variant is marked as default. If you need other
-version or variant, load the particular version. To obtain the list of
-available versions use
-
- $ module avail MATLAB
-
-If you need to use the Matlab GUI to prepare your Matlab programs, you
-can use Matlab directly on the login nodes. But for all computations use
-Matlab on the compute nodes via PBS Pro scheduler.
-
-If you require the Matlab GUI, please follow the general informations
-about [running graphical
-applications](../../../get-started-with-it4innovations/accessing-the-clusters/graphical-user-interface/x-window-system/x-window-and-vnc.html).
-
-Matlab GUI is quite slow using the X forwarding built in the PBS (qsub
--X), so using X11 display redirection either via SSH or directly by
-xauth (please see the "GUI Applications on Compute Nodes over VNC" part
-[here](../../../get-started-with-it4innovations/accessing-the-clusters/graphical-user-interface/x-window-system/x-window-and-vnc.html))
-is recommended.
-
-To run Matlab with GUI, use
-
- $ matlab
-
-To run Matlab in text mode, without the Matlab Desktop GUI environment,
-use
-
- $ matlab -nodesktop -nosplash
-
-plots, images, etc... will be still available.
-
-Running parallel Matlab using Distributed Computing Toolbox / Engine
-------------------------------------------------------------------------
-
-Distributed toolbox is available only for the EDU variant
-
-The MPIEXEC mode available in previous versions is no longer available
-in MATLAB 2015. Also, the programming interface has changed. Refer
-to [Release
-Notes](http://www.mathworks.com/help/distcomp/release-notes.html#buanp9e-1).
-
-Delete previously used file mpiLibConf.m, we have observed crashes when
-using Intel MPI.
-
-To use Distributed Computing, you first need to setup a parallel
-profile. We have provided the profile for you, you can either import it
-in MATLAB command line:
-
- > parallel.importProfile('/apps/all/MATLAB/2015b-EDU/SalomonPBSPro.settings')
-
- ans =
-
- SalomonPBSProÂ
-
-Or in the GUI, go to tab HOME -> Parallel -> Manage Cluster
-Profiles..., click Import and navigate to :
-
-/apps/all/MATLAB/2015b-EDU/SalomonPBSPro.settings
-
-With the new mode, MATLAB itself launches the workers via PBS, so you
-can either use interactive mode or a batch mode on one node, but the
-actual parallel processing will be done in a separate job started by
-MATLAB itself. Alternatively, you can use "local" mode to run parallel
-code on just a single node.
-
-### Parallel Matlab interactive session
-
-Following example shows how to start interactive session with support
-for Matlab GUI. For more information about GUI based applications on
-Anselm see [this
-page](../../../get-started-with-it4innovations/accessing-the-clusters/graphical-user-interface/x-window-system/x-window-and-vnc.html).
-
- $ xhost +
- $ qsub -I -v DISPLAY=$(uname -n):$(echo $DISPLAY | cut -d ':' -f 2) -A NONE-0-0 -q qexp -l select=1 -l walltime=00:30:00
- -l feature__matlab__MATLAB=1
-
-This qsub command example shows how to run Matlab on a single node.
-
-The second part of the command shows how to request all necessary
-licenses. In this case 1 Matlab-EDU license and 48 Distributed Computing
-Engines licenses.
-
-Once the access to compute nodes is granted by PBS, user can load
-following modules and start Matlab:Â
-
- r1i0n17$ module load MATLAB/2015a-EDU
- r1i0n17$ matlab &
-
-### Parallel Matlab batch job in Local mode
-
-To run matlab in batch mode, write an matlab script, then write a bash
-jobscript and execute via the qsub command. By default, matlab will
-execute one matlab worker instance per allocated core.
-
- #!/bin/bash
- #PBS -A PROJECT ID
- #PBS -q qprod
- #PBS -l select=1:ncpus=24:mpiprocs=24:ompthreads=1
-
- # change to shared scratch directory
- SCR=/scratch/work/user/$USER/$PBS_JOBID
- mkdir -p $SCR ; cd $SCR || exit
-
- # copy input file to scratch
- cp $PBS_O_WORKDIR/matlabcode.m .
-
- # load modules
- module load MATLAB/2015a-EDU
-
- # execute the calculation
- matlab -nodisplay -r matlabcode > output.out
-
- # copy output file to home
- cp output.out $PBS_O_WORKDIR/.
-
-This script may be submitted directly to the PBS workload manager via
-the qsub command. The inputs and matlab script are in matlabcode.m
-file, outputs in output.out file. Note the missing .m extension in the
-matlab -r matlabcodefile call, **the .m must not be included**. Note
-that the **shared /scratch must be used**. Further, it is **important to
-include quit** statement at the end of the matlabcode.m script.
-
-Submit the jobscript using qsub
-
- $ qsub ./jobscript
-
-### Parallel Matlab Local mode program example
-
-The last part of the configuration is done directly in the user Matlab
-script before Distributed Computing Toolbox is started.
-
- cluster = parcluster('local')
-
-This script creates scheduler object "cluster" of type "local" that
-starts workers locally.Â
-
-Please note: Every Matlab script that needs to initialize/use matlabpool
-has to contain these three lines prior to calling parpool(sched, ...)
-function.Â
-
-The last step is to start matlabpool with "cluster" object and correct
-number of workers. We have 24 cores per node, so we start 24 workers.
-
- parpool(cluster,24);
-
-
- ... parallel code ...
-
-
- parpool close
-
-The complete example showing how to use Distributed Computing Toolbox in
-local mode is shown here.Â
-
- cluster = parcluster('local');
- cluster
-
- parpool(cluster,24);
-
- n=2000;
-
- W = rand(n,n);
- W = distributed(W);
- x = (1:n)';
- x = distributed(x);
- spmd
- [~, name] = system('hostname')
- Â Â Â
- Â Â Â T = W*x; % Calculation performed on labs, in parallel.
- Â Â Â Â Â Â Â Â Â Â Â Â % T and W are both codistributed arrays here.
- end
- T;
- whos        % T and W are both distributed arrays here.
-
- parpool close
- quit
-
-You can copy and paste the example in a .m file and execute. Note that
-the parpool size should correspond to **total number of cores**
-available on allocated nodes.
-
-### Parallel Matlab Batch job using PBS mode (workers spawned in a separate job)
-
-This mode uses PBS scheduler to launch the parallel pool. It uses the
-SalomonPBSPro profile that needs to be imported to Cluster Manager, as
-mentioned before. This methodod uses MATLAB's PBS Scheduler interface -
-it spawns the workers in a separate job submitted by MATLAB using qsub.
-
-This is an example of m-script using PBS mode:
-
- cluster = parcluster('SalomonPBSPro');
- set(cluster, 'SubmitArguments', '-A OPEN-0-0');
- set(cluster, 'ResourceTemplate', '-q qprod -l select=10:ncpus=24');
- set(cluster, 'NumWorkers', 240);
-
- pool = parpool(cluster,240);
-
- n=2000;
-
- W = rand(n,n);
- W = distributed(W);
- x = (1:n)';
- x = distributed(x);
- spmd
- [~, name] = system('hostname')
-
- T = W*x; % Calculation performed on labs, in parallel.
- % T and W are both codistributed arrays here.
- end
- whos % T and W are both distributed arrays here.
-
- % shut down parallel pool
- delete(pool)
-
-Note that we first construct a cluster object using the imported
-profile, then set some important options, namely : SubmitArguments,
-where you need to specify accounting id, and ResourceTemplate, where you
-need to specify number of nodes to run the job.Â
-
-You can start this script using batch mode the same way as in Local mode
-example.
-
-### Parallel Matlab Batch with direct launch (workers spawned within the existing job)
-
-This method is a "hack" invented by us to emulate the mpiexec
-functionality found in previous MATLAB versions. We leverage the MATLAB
-Generic Scheduler interface, but instead of submitting the workers to
-PBS, we launch the workers directly within the running job, thus we
-avoid the issues with master script and workers running in separate jobs
-(issues with license not available, waiting for the worker's job to
-spawn etc.)
-
-Please note that this method is experimental.
-
-For this method, you need to use SalomonDirect profile, import it
-using [the same way as
-SalomonPBSPro](matlab.html#running-parallel-matlab-using-distributed-computing-toolbox---engine)Â
-
-This is an example of m-script using direct mode:
-
- parallel.importProfile('/apps/all/MATLAB/2015b-EDU/SalomonDirect.settings')
- cluster = parcluster('SalomonDirect');
- set(cluster, 'NumWorkers', 48);
-
- pool = parpool(cluster, 48);
-
- n=2000;
-
- W = rand(n,n);
- W = distributed(W);
- x = (1:n)';
- x = distributed(x);
- spmd
- [~, name] = system('hostname')
-
- T = W*x; % Calculation performed on labs, in parallel.
- % T and W are both codistributed arrays here.
- end
- whos % T and W are both distributed arrays here.
-
- % shut down parallel pool
- delete(pool)
-
-### Non-interactive Session and Licenses
-
-If you want to run batch jobs with Matlab, be sure to request
-appropriate license features with the PBS Pro scheduler, at least the "
--l __feature__matlab__MATLAB=1" for EDU variant of Matlab. More
-information about how to check the license features states and how to
-request them with PBS Pro, please [look
-here](../../../anselm-cluster-documentation/software/isv_licenses.html).
-
-The licensing feature of PBS is currently disabled.
-
-In case of non-interactive session please read the [following
-information](../../../anselm-cluster-documentation/software/isv_licenses.html)
-on how to modify the qsub command to test for available licenses prior
-getting the resource allocation.
-
-### Matlab Distributed Computing Engines start up time
-
-Starting Matlab workers is an expensive process that requires certain
-amount of time. For your information please see the following table:
-
- |compute nodes|number of workers|start-up time[s]|
- |---|---|---|
- |16|384|831|
- |8|192|807|
- |4|96|483|
- |2|48|16|
-
-MATLAB on UV2000Â
------------------
-
-UV2000 machine available in queue "qfat" can be used for MATLAB
-computations. This is a SMP NUMA machine with large amount of RAM, which
-can be beneficial for certain types of MATLAB jobs. CPU cores are
-allocated in chunks of 8 for this machine.
-
-You can use MATLAB on UV2000 in two parallel modes :
-
-### Threaded mode
-
-Since this is a SMP machine, you can completely avoid using Parallel
-Toolbox and use only MATLAB's threading. MATLAB will automatically
-detect the number of cores you have allocated and will setÂ
-maxNumCompThreads accordingly and certain
-operations, such as fft, , eig, svd,
-etc. will be automatically run in threads. The advantage of this mode is
-that you don't need to modify your existing sequential codes.
-
-### Local cluster mode
-
-You can also use Parallel Toolbox on UV2000. Use l[ocal cluster
-mode](matlab.html#parallel-matlab-batch-job-in-local-mode),
-"SalomonPBSPro" profile will not work.
-
-Â
-
-Â
-
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-Octave
-======
-
-
-
-GNU Octave is a high-level interpreted language, primarily intended for
-numerical computations. It provides capabilities for the numerical
-solution of linear and nonlinear problems, and for performing other
-numerical experiments. It also provides extensive graphics capabilities
-for data visualization and manipulation. Octave is normally used through
-its interactive command line interface, but it can also be used to write
-non-interactive programs. The Octave language is quite similar to Matlab
-so that most programs are easily portable. Read more on
-<http://www.gnu.org/software/octave/>***
-
-Two versions of octave are available on the cluster, via module
-
- Status Version module
- ------ |---|---|---- --------
- **Stable** Octave 3.8.2 Octave
-
-Â
-
- $ module load Octave
-
-The octave on the cluster is linked to highly optimized MKL mathematical
-library. This provides threaded parallelization to many octave kernels,
-notably the linear algebra subroutines. Octave runs these heavy
-calculation kernels without any penalty. By default, octave would
-parallelize to 24 threads. You may control the threads by setting the
-OMP_NUM_THREADS environment variable.
-
-To run octave interactively, log in with ssh -X parameter for X11
-forwarding. Run octave:
-
- $ octave
-
-To run octave in batch mode, write an octave script, then write a bash
-jobscript and execute via the qsub command. By default, octave will use
-16 threads when running MKL kernels.
-
- #!/bin/bash
-
- # change to local scratch directory
- mkdir -p /scratch/work/user/$USER/$PBS_JOBID
- cd /scratch/work/user/$USER/$PBS_JOBID || exit
-
- # copy input file to scratch
- cp $PBS_O_WORKDIR/octcode.m .
-
- # load octave module
- module load Octave
-
- # execute the calculation
- octave -q --eval octcode > output.out
-
- # copy output file to home
- cp output.out $PBS_O_WORKDIR/.
-
- #exit
- exit
-
-This script may be submitted directly to the PBS workload manager via
-the qsub command. The inputs are in octcode.m file, outputs in
-output.out file. See the single node jobscript example in the [Job
-execution
-section](../../resource-allocation-and-job-execution.html).
-
-The octave c compiler mkoctfile calls the GNU gcc 4.8.1 for compiling
-native c code. This is very useful for running native c subroutines in
-octave environment.
-
-$ mkoctfile -v
-
-Octave may use MPI for interprocess communication
-This functionality is currently not supported on the cluster cluster. In
-case you require the octave interface to MPI, please contact our
-[cluster support](https://support.it4i.cz/rt/).
-
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+++ /dev/null
@@ -1,460 +0,0 @@
-R
-=
-
-
-
-Introduction
-------------
-
-The R is a language and environment for statistical computing and
-graphics. R provides a wide variety of statistical (linear and
-nonlinear modelling, classical statistical tests, time-series analysis,
-classification, clustering, ...) and graphical techniques, and is highly
-extensible.
-
-One of R's strengths is the ease with which well-designed
-publication-quality plots can be produced, including mathematical
-symbols and formulae where needed. Great care has been taken over the
-defaults for the minor design choices in graphics, but the user retains
-full control.
-
-Another convenience is the ease with which the C code or third party
-libraries may be integrated within R.
-
-Extensive support for parallel computing is available within R.
-
-Read more on <http://www.r-project.org/>,
-<http://cran.r-project.org/doc/manuals/r-release/R-lang.html>
-
-Modules
--------
-
-**The R version 3.1.1 is available on the cluster, along with GUI
-interface Rstudio**
-
- |Application|Version|module|
- ------- |---|---|---- ---------------------
- |**R**|R 3.1.1|R/3.1.1-intel-2015b|
- |**Rstudio**|Rstudio 0.97|Rstudio|
-
- $ module load R
-
-Execution
----------
-
-The R on Anselm is linked to highly optimized MKL mathematical
-library. This provides threaded parallelization to many R kernels,
-notably the linear algebra subroutines. The R runs these heavy
-calculation kernels without any penalty. By default, the R would
-parallelize to 24 threads. You may control the threads by setting the
-OMP_NUM_THREADS environment variable.
-
-### Interactive execution
-
-To run R interactively, using Rstudio GUI, log in with ssh -X parameter
-for X11 forwarding. Run rstudio:
-
- $ module load Rstudio
- $ rstudio
-
-### Batch execution
-
-To run R in batch mode, write an R script, then write a bash jobscript
-and execute via the qsub command. By default, R will use 24 threads when
-running MKL kernels.
-
-Example jobscript:
-
- #!/bin/bash
-
- # change to local scratch directory
- cd /lscratch/$PBS_JOBID || exit
-
- # copy input file to scratch
- cp $PBS_O_WORKDIR/rscript.R .
-
- # load R module
- module load R
-
- # execute the calculation
- R CMD BATCH rscript.R routput.out
-
- # copy output file to home
- cp routput.out $PBS_O_WORKDIR/.
-
- #exit
- exit
-
-This script may be submitted directly to the PBS workload manager via
-the qsub command. The inputs are in rscript.R file, outputs in
-routput.out file. See the single node jobscript example in the [Job
-execution
-section](../../resource-allocation-and-job-execution/job-submission-and-execution.html).
-
-Parallel R
-----------
-
-Parallel execution of R may be achieved in many ways. One approach is
-the implied parallelization due to linked libraries or specially enabled
-functions, as [described
-above](r.html#interactive-execution). In the following
-sections, we focus on explicit parallelization, where parallel
-constructs are directly stated within the R script.
-
-Package parallel
---------------------
-
-The package parallel provides support for parallel computation,
-including by forking (taken from package multicore), by sockets (taken
-from package snow) and random-number generation.
-
-The package is activated this way:
-
- $ R
- > library(parallel)
-
-More information and examples may be obtained directly by reading the
-documentation available in R
-
- > ?parallel
- > library(help = "parallel")
- > vignette("parallel")
-
-Download the package
-[parallell](package-parallel-vignette) vignette.
-
-The forking is the most simple to use. Forking family of functions
-provide parallelized, drop in replacement for the serial apply() family
-of functions.
-
-Forking via package parallel provides functionality similar to OpenMP
-construct
-#omp parallel for
-
-Only cores of single node can be utilized this way!
-
-Forking example:
-
- library(parallel)
-
- #integrand function
- f <- function(i,h) {
- x <- h*(i-0.5)
- return (4/(1 + x*x))
- }
-
- #initialize
- size <- detectCores()
-
- while (TRUE)
- {
- #read number of intervals
- cat("Enter the number of intervals: (0 quits) ")
- fp<-file("stdin"); n<-scan(fp,nmax=1); close(fp)
-
- if(n<=0) break
-
- #run the calculation
- n <- max(n,size)
- h <- 1.0/n
-
- i <- seq(1,n);
- pi3 <- h*sum(simplify2array(mclapply(i,f,h,mc.cores=size)));
-
- #print results
- cat(sprintf("Value of PI %16.14f, diff= %16.14fn",pi3,pi3-pi))
- }
-
-The above example is the classic parallel example for calculating the
-number π. Note the **detectCores()** and **mclapply()** functions.
-Execute the example as:
-
- $ R --slave --no-save --no-restore -f pi3p.R
-
-Every evaluation of the integrad function runs in parallel on different
-process.
-
-Package Rmpi
-------------
-
-package Rmpi provides an interface (wrapper) to MPI APIs.
-
-It also provides interactive R slave environment. On the cluster, Rmpi
-provides interface to the
-[OpenMPI](../mpi-1/Running_OpenMPI.html).
-
-Read more on Rmpi at <http://cran.r-project.org/web/packages/Rmpi/>,
-reference manual is available at
-<http://cran.r-project.org/web/packages/Rmpi/Rmpi.pdf>
-
-When using package Rmpi, both openmpi and R modules must be loaded
-
- $ module load OpenMPI
- $ module load R
-
-Rmpi may be used in three basic ways. The static approach is identical
-to executing any other MPI programm. In addition, there is Rslaves
-dynamic MPI approach and the mpi.apply approach. In the following
-section, we will use the number π integration example, to illustrate all
-these concepts.
-
-### static Rmpi
-
-Static Rmpi programs are executed via mpiexec, as any other MPI
-programs. Number of processes is static - given at the launch time.
-
-Static Rmpi example:
-
- library(Rmpi)
-
- #integrand function
- f <- function(i,h) {
- x <- h*(i-0.5)
- return (4/(1 + x*x))
- }
-
- #initialize
- invisible(mpi.comm.dup(0,1))
- rank <- mpi.comm.rank()
- size <- mpi.comm.size()
- n<-0
-
- while (TRUE)
- {
- #read number of intervals
- if (rank==0) {
- cat("Enter the number of intervals: (0 quits) ")
- fp<-file("stdin"); n<-scan(fp,nmax=1); close(fp)
- }
-
- #broadcat the intervals
- n <- mpi.bcast(as.integer(n),type=1)
-
- if(n<=0) break
-
- #run the calculation
- n <- max(n,size)
- h <- 1.0/n
-
- i <- seq(rank+1,n,size);
- mypi <- h*sum(sapply(i,f,h));
-
- pi3 <- mpi.reduce(mypi)
-
- #print results
- if (rank==0) cat(sprintf("Value of PI %16.14f, diff= %16.14fn",pi3,pi3-pi))
- }
-
- mpi.quit()
-
-The above is the static MPI example for calculating the number π. Note
-the **library(Rmpi)** and **mpi.comm.dup()** function calls.
-Execute the example as:
-
- $ mpirun R --slave --no-save --no-restore -f pi3.R
-
-### dynamic Rmpi
-
-Dynamic Rmpi programs are executed by calling the R directly. OpenMPI
-module must be still loaded. The R slave processes will be spawned by a
-function call within the Rmpi program.
-
-Dynamic Rmpi example:
-
- #integrand function
- f <- function(i,h) {
- x <- h*(i-0.5)
- return (4/(1 + x*x))
- }
-
- #the worker function
- workerpi <- function()
- {
- #initialize
- rank <- mpi.comm.rank()
- size <- mpi.comm.size()
- n<-0
-
- while (TRUE)
- {
- #read number of intervals
- if (rank==0) {
- cat("Enter the number of intervals: (0 quits) ")
- fp<-file("stdin"); n<-scan(fp,nmax=1); close(fp)
- }
-
- #broadcat the intervals
- n <- mpi.bcast(as.integer(n),type=1)
-
- if(n<=0) break
-
- #run the calculation
- n <- max(n,size)
- h <- 1.0/n
-
- i <- seq(rank+1,n,size);
- mypi <- h*sum(sapply(i,f,h));
-
- pi3 <- mpi.reduce(mypi)
-
- #print results
- if (rank==0) cat(sprintf("Value of PI %16.14f, diff= %16.14fn",pi3,pi3-pi))
- }
- }
-
- #main
- library(Rmpi)
-
- cat("Enter the number of slaves: ")
- fp<-file("stdin"); ns<-scan(fp,nmax=1); close(fp)
-
- mpi.spawn.Rslaves(nslaves=ns)
- mpi.bcast.Robj2slave(f)
- mpi.bcast.Robj2slave(workerpi)
-
- mpi.bcast.cmd(workerpi())
- workerpi()
-
- mpi.quit()
-
-The above example is the dynamic MPI example for calculating the number
-Ď€. Both master and slave processes carry out the calculation. Note the
-mpi.spawn.Rslaves(), mpi.bcast.Robj2slave()** and the
-mpi.bcast.cmd()** function calls.
-Execute the example as:
-
- $ mpirun -np 1 R --slave --no-save --no-restore -f pi3Rslaves.R
-
-Note that this method uses MPI_Comm_spawn (Dynamic process feature of
-MPI-2) to start the slave processes - the master process needs to be
-launched with MPI. In general, Dynamic processes are not well supported
-among MPI implementations, some issues might arise. Also, environment
-variables are not propagated to spawned processes, so they will not see
-paths from modules.
-### mpi.apply Rmpi
-
-mpi.apply is a specific way of executing Dynamic Rmpi programs.
-
-mpi.apply() family of functions provide MPI parallelized, drop in
-replacement for the serial apply() family of functions.
-
-Execution is identical to other dynamic Rmpi programs.
-
-mpi.apply Rmpi example:
-
- #integrand function
- f <- function(i,h) {
- x <- h*(i-0.5)
- return (4/(1 + x*x))
- }
-
- #the worker function
- workerpi <- function(rank,size,n)
- {
- #run the calculation
- n <- max(n,size)
- h <- 1.0/n
-
- i <- seq(rank,n,size);
- mypi <- h*sum(sapply(i,f,h));
-
- return(mypi)
- }
-
- #main
- library(Rmpi)
-
- cat("Enter the number of slaves: ")
- fp<-file("stdin"); ns<-scan(fp,nmax=1); close(fp)
-
- mpi.spawn.Rslaves(nslaves=ns)
- mpi.bcast.Robj2slave(f)
- mpi.bcast.Robj2slave(workerpi)
-
- while (TRUE)
- {
- #read number of intervals
- cat("Enter the number of intervals: (0 quits) ")
- fp<-file("stdin"); n<-scan(fp,nmax=1); close(fp)
- if(n<=0) break
-
- #run workerpi
- i=seq(1,2*ns)
- pi3=sum(mpi.parSapply(i,workerpi,2*ns,n))
-
- #print results
- cat(sprintf("Value of PI %16.14f, diff= %16.14fn",pi3,pi3-pi))
- }
-
- mpi.quit()
-
-The above is the mpi.apply MPI example for calculating the number π.
-Only the slave processes carry out the calculation. Note the
-mpi.parSapply(), ** function call. The package
-parallel
-[example](r.html#package-parallel)[above](r.html#package-parallel){.anchor
-may be trivially adapted (for much better performance) to this structure
-using the mclapply() in place of mpi.parSapply().
-
-Execute the example as:
-
- $ mpirun -np 1 R --slave --no-save --no-restore -f pi3parSapply.R
-
-Combining parallel and Rmpi
----------------------------
-
-Currently, the two packages can not be combined for hybrid calculations.
-
-Parallel execution
-------------------
-
-The R parallel jobs are executed via the PBS queue system exactly as any
-other parallel jobs. User must create an appropriate jobscript and
-submit via the **qsub**
-
-Example jobscript for [static Rmpi](r.html#static-rmpi)
-parallel R execution, running 1 process per core:
-
- #!/bin/bash
- #PBS -q qprod
- #PBS -N Rjob
- #PBS -l select=100:ncpus=24:mpiprocs=24:ompthreads=1
-
- # change to scratch directory
- SCRDIR=/scratch/work/user/$USER/myjob
- cd $SCRDIR || exit
-
- # copy input file to scratch
- cp $PBS_O_WORKDIR/rscript.R .
-
- # load R and openmpi module
- module load R
- module load OpenMPI
-
- # execute the calculation
- mpiexec -bycore -bind-to-core R --slave --no-save --no-restore -f rscript.R
-
- # copy output file to home
- cp routput.out $PBS_O_WORKDIR/.
-
- #exit
- exit
-
-For more information about jobscripts and MPI execution refer to the
-[Job
-submission](../../resource-allocation-and-job-execution/job-submission-and-execution.html)
-and general [MPI](../mpi-1.html) sections.
-
-Xeon Phi Offload
-----------------
-
-By leveraging MKL, R can accelerate certain computations, most notably
-linear algebra operations on the Xeon Phi accelerator by using Automated
-Offload. To use MKL Automated Offload, you need to first set this
-environment variable before R execution :
-
- $ export MKL_MIC_ENABLE=1
-
-[Read more about automatic
-offload](../intel-xeon-phi.html)
-
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-Operating System
-================
-
-The operating system, deployed on Salomon cluster
-
-
-
-The operating system on Salomon is Linux - CentOS 6.6.
-
-The CentOS Linux distribution is a stable, predictable, manageable
-and reproducible platform derived from the sources of Red Hat Enterprise
-Linux (RHEL).
-
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-CESNET Data Storage
-===================
-
-
-
-Introduction
-------------
-
-Do not use shared filesystems at IT4Innovations as a backup for large
-amount of data or long-term archiving purposes.
-
-The IT4Innovations does not provide storage capacity for data archiving.
-Academic staff and students of research institutions in the Czech
-Republic can use [CESNET Storage
-service](https://du.cesnet.cz/).
-
-The CESNET Storage service can be used for research purposes, mainly by
-academic staff and students of research institutions in the Czech
-Republic.
-
-User of data storage CESNET (DU) association can become organizations or
-an individual person who is either in the current employment
-relationship (employees) or the current study relationship (students) to
-a legal entity (organization) that meets the “Principles for access to
-CESNET Large infrastructure (Access Policy)”.
-
-User may only use data storage CESNET for data transfer and storage
-which are associated with activities in science, research, development,
-the spread of education, culture and prosperity. In detail see
-“Acceptable Use Policy CESNET Large Infrastructure (Acceptable Use
-Policy, AUP)”.
-
-The service is documented at
-<https://du.cesnet.cz/wiki/doku.php/en/start>. For special requirements
-please contact directly CESNET Storage Department via e-mail
-[du-support(at)cesnet.cz](mailto:du-support@cesnet.cz).
-
-The procedure to obtain the CESNET access is quick and trouble-free.
-
-(source
-[https://du.cesnet.cz/](https://du.cesnet.cz/wiki/doku.php/en/start "CESNET Data Storage"))
-
-CESNET storage access
----------------------
-
-### Understanding Cesnet storage
-
-It is very important to understand the Cesnet storage before uploading
-data. Please read
-<https://du.cesnet.cz/en/navody/home-migrace-plzen/start> first.
-
-Once registered for CESNET Storage, you may [access the
-storage](https://du.cesnet.cz/en/navody/faq/start) in
-number of ways. We recommend the SSHFS and RSYNC methods.
-
-### SSHFS Access
-
-SSHFS: The storage will be mounted like a local hard drive
-
-The SSHFSÂ provides a very convenient way to access the CESNET Storage.
-The storage will be mounted onto a local directory, exposing the vast
-CESNET Storage as if it was a local removable harddrive. Files can be
-than copied in and out in a usual fashion.
-
-First, create the mountpoint
-
- $ mkdir cesnet
-
-Mount the storage. Note that you can choose among the ssh.du1.cesnet.cz
-(Plzen), ssh.du2.cesnet.cz (Jihlava), ssh.du3.cesnet.cz (Brno)
-Mount tier1_home **(only 5120M !)**:
-
- $ sshfs username@ssh.du1.cesnet.cz:. cesnet/
-
-For easy future access from Anselm, install your public key
-
- $ cp .ssh/id_rsa.pub cesnet/.ssh/authorized_keys
-
-Mount tier1_cache_tape for the Storage VO:
-
- $ sshfs username@ssh.du1.cesnet.cz:/cache_tape/VO_storage/home/username cesnet/
-
-View the archive, copy the files and directories in and out
-
- $ ls cesnet/
- $ cp -a mydir cesnet/.
- $ cp cesnet/myfile .
-
-Once done, please remember to unmount the storage
-
- $ fusermount -u cesnet
-
-### Rsync access
-
-Rsync provides delta transfer for best performance, can resume
-interrupted transfers
-
-Rsync is a fast and extraordinarily versatile file copying tool. It is
-famous for its delta-transfer algorithm, which reduces the amount of
-data sent over the network by sending only the differences between the
-source files and the existing files in the destination. Rsync is widely
-used for backups and mirroring and as an improved copy command for
-everyday use.
-
-Rsync finds files that need to be transferred using a "quick check"
-algorithm (by default) that looks for files that have changed in size or
-in last-modified time. Any changes in the other preserved attributes
-(as requested by options) are made on the destination file directly when
-the quick check indicates that the file's data does not need to be
-updated.
-
-More about Rsync at
-<https://du.cesnet.cz/en/navody/rsync/start#pro_bezne_uzivatele>
-
-Transfer large files to/from Cesnet storage, assuming membership in the
-Storage VO
-
- $ rsync --progress datafile username@ssh.du1.cesnet.cz:VO_storage-cache_tape/.
- $ rsync --progress username@ssh.du1.cesnet.cz:VO_storage-cache_tape/datafile .
-
-Transfer large directories to/from Cesnet storage, assuming membership
-in the Storage VO
-
- $ rsync --progress -av datafolder username@ssh.du1.cesnet.cz:VO_storage-cache_tape/.
- $ rsync --progress -av username@ssh.du1.cesnet.cz:VO_storage-cache_tape/datafolder .
-
-Transfer rates of about 28MB/s can be expected.
-
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-Storage
-=======
-
-
-
-Introduction
-------------
-
-There are two main shared file systems on Salomon cluster, the [HOME](storage.html#home)and [SCRATCH](storage.html#shared-filesystems).
-
-All login and compute nodes may access same data on shared filesystems.
-Compute nodes are also equipped with local (non-shared) scratch, ramdisk
-and tmp filesystems.
-
-Policy (in a nutshell)
-----------------------
-
-Use [ for your most valuable data
-and programs.
-Use [WORK](storage.html#work) for your large project
-files
-Use [TEMP](storage.html#temp) for large scratch data.
-
-Do not use for [archiving](storage.html#archiving)!
-
-Archiving
--------------
-
-Please don't use shared filesystems as a backup for large amount of data
-or long-term archiving mean. The academic staff and students of research
-institutions in the Czech Republic can use [CESNET storage
-service](../../anselm-cluster-documentation/storage-1/cesnet-data-storage.html),
-which is available via SSHFS.
-
-Shared Filesystems
-----------------------
-
-Salomon computer provides two main shared filesystems, the [
-HOME
-filesystem](storage.html#home-filesystem) and the
-[SCRATCH filesystem](storage.html#scratch-filesystem). The
-SCRATCH filesystem is partitioned to [WORK and TEMP
-workspaces](storage.html#shared-workspaces). The HOME
-filesystem is realized as a tiered NFSÂ disk storage. The SCRATCH
-filesystem is realized as a parallel Lustre filesystem. Both shared file
-systems are accessible via the Infiniband network. Extended ACLs are
-provided on both HOME/SCRATCH filesystems for the purpose of sharing
-data with other users using fine-grained control.
-
-###HOME filesystem
-
-The HOME filesystem is realized as a Tiered filesystem, exported via
-NFS. The first tier has capacity 100TB, second tier has capacity 400TB.
-The filesystem is available on all login and computational nodes. The
-Home filesystem hosts the [HOME
-workspace](storage.html#home).
-
-###SCRATCH filesystem
-
-The architecture of Lustre on Salomon is composed of two metadata
-servers (MDS) and six data/object storage servers (OSS). Accessible
-capacity is 1.69 PB, shared among all users. The SCRATCH filesystem
-hosts the [WORK and TEMP
-workspaces](storage.html#shared-workspaces).
-
- Configuration of the SCRATCH Lustre storage
-
-
-- SCRATCH Lustre object storage
-
-
- - Disk array SFA12KX
- - 540 4TB SAS 7.2krpm disks
- - 54 OSTs of 10 disks in RAID6 (8+2)
- - 15 hot-spare disks
- - 4x 400GB SSD cache
-
-
-
-- SCRATCH Lustre metadata storage
-
-
- - Disk array EF3015
- - 12 600GB SAS 15krpm disks
-
-
-
-### Understanding the Lustre Filesystems
-
-(source <http://www.nas.nasa.gov>)
-
-A user file on the Lustre filesystem can be divided into multiple chunks
-(stripes) and stored across a subset of the object storage targets
-(OSTs) (disks). The stripes are distributed among the OSTs in a
-round-robin fashion to ensure load balancing.
-
-When a client (a compute
-node from your job) needs to create
-or access a file, the client queries the metadata server (
-MDS) and the metadata target (
-MDT) for the layout and location of the
-[file's
-stripes](http://www.nas.nasa.gov/hecc/support/kb/Lustre_Basics_224.html#striping).
-Once the file is opened and the client obtains the striping information,
-the MDS is no longer involved in the
-file I/O process. The client interacts directly with the object storage
-servers (OSSes) and OSTs to perform I/O operations such as locking, disk
-allocation, storage, and retrieval.
-
-If multiple clients try to read and write the same part of a file at the
-same time, the Lustre distributed lock manager enforces coherency so
-that all clients see consistent results.
-
-There is default stripe configuration for Salomon Lustre filesystems.
-However, users can set the following stripe parameters for their own
-directories or files to get optimum I/O performance:
-
-1. stripe_size: the size of the chunk in bytes; specify with k, m, or
- g to use units of KB, MB, or GB, respectively; the size must be an
- even multiple of 65,536 bytes; default is 1MB for all Salomon Lustre
- filesystems
-2. stripe_count the number of OSTs to stripe across; default is 1 for
- Salomon Lustre filesystems one can specify -1 to use all OSTs in
- the filesystem.
-3. stripe_offset The index of the
- OST where the first stripe is to be
- placed; default is -1 which results in random selection; using a
- non-default value is NOT recommended.
-
-Â
-
-Setting stripe size and stripe count correctly for your needs may
-significantly impact the I/O performance you experience.
-
-Use the lfs getstripe for getting the stripe parameters. Use the lfs
-setstripe command for setting the stripe parameters to get optimal I/O
-performance The correct stripe setting depends on your needs and file
-access patterns.Â
-
-`
-$ lfs getstripe dir|filename
-$ lfs setstripe -s stripe_size -c stripe_count -o stripe_offset dir|filename
-`
-
-Example:
-
-`
-$ lfs getstripe /scratch/work/user/username
-/scratch/work/user/username
-stripe_count: 1 stripe_size: 1048576 stripe_offset: -1
-
-$ lfs setstripe -c -1 /scratch/work/user/username/
-$ lfs getstripe /scratch/work/user/username/
-/scratch/work/user/username/
-stripe_count: -1 stripe_size: 1048576 stripe_offset: -1
-`
-
-In this example, we view current stripe setting of the
-/scratch/username/ directory. The stripe count is changed to all OSTs,
-and verified. All files written to this directory will be striped over
-all (54) OSTs
-
-Use lfs check OSTs to see the number and status of active OSTs for each
-filesystem on Salomon. Learn more by reading the man page
-
-`
-$ lfs check osts
-$ man lfs
-`
-
-### Hints on Lustre Stripping
-
-Increase the stripe_count for parallel I/O to the same file.
-
-When multiple processes are writing blocks of data to the same file in
-parallel, the I/O performance for large files will improve when the
-stripe_count is set to a larger value. The stripe count sets the number
-of OSTs the file will be written to. By default, the stripe count is set
-to 1. While this default setting provides for efficient access of
-metadata (for example to support the ls -l command), large files should
-use stripe counts of greater than 1. This will increase the aggregate
-I/O bandwidth by using multiple OSTs in parallel instead of just one. A
-rule of thumb is to use a stripe count approximately equal to the number
-of gigabytes in the file.
-
-Another good practice is to make the stripe count be an integral factor
-of the number of processes performing the write in parallel, so that you
-achieve load balance among the OSTs. For example, set the stripe count
-to 16 instead of 15 when you have 64 processes performing the writes.
-
-Using a large stripe size can improve performance when accessing very
-large files
-
-Large stripe size allows each client to have exclusive access to its own
-part of a file. However, it can be counterproductive in some cases if it
-does not match your I/O pattern. The choice of stripe size has no effect
-on a single-stripe file.
-
-Read more on
-<http://wiki.lustre.org/manual/LustreManual20_HTML/ManagingStripingFreeSpace.html>
-
-Disk usage and quota commands
-------------------------------------------
-
-User quotas on the Lustre file systems (SCRATCH) can be checked
-and reviewed using following command:
-
-`
-$ lfs quota dir
-`
-
-Example for Lustre SCRATCH directory:
-
-`
-$ lfs quota /scratch
-Disk quotas for user user001 (uid 1234):
- Filesystem kbytes quota limit grace files quota limit grace
-  /scratch    8    0 100000000000    -    3    0    0    -
-Disk quotas for group user001 (gid 1234):
- Filesystem kbytes quota limit grace files quota limit grace
- /scratch    8    0    0    -    3    0    0    -
-`
-
-In this example, we view current quota size limit of 100TB and 8KB
-currently used by user001.
-
-HOME directory is mounted via NFS, so a different command must be used
-to obtain quota information:
-
- Â $ quota
-
-Example output:
-
- $ quota
- Disk quotas for user vop999 (uid 1025):
- Filesystem blocks quota limit grace files quota limit grace
- home-nfs-ib.salomon.it4i.cz:/home
- 28 0 250000000 10 0 500000
-
-To have a better understanding of where the space is exactly used, you
-can use following command to find out.
-
-`
-$ du -hs dir
-`
-
-Example for your HOME directory:
-
-`
-$ cd /home
-$ du -hs * .[a-zA-z0-9]* | grep -E "[0-9]*G|[0-9]*M" | sort -hr
-258M cuda-samples
-15M .cache
-13M .mozilla
-5,5M .eclipse
-2,7M .idb_13.0_linux_intel64_app
-`
-
-This will list all directories which are having MegaBytes or GigaBytes
-of consumed space in your actual (in this example HOME) directory. List
-is sorted in descending order from largest to smallest
-files/directories.
-
-To have a better understanding of previous commands, you can read
-manpages.
-
-`
-$ man lfs
-`
-
-`
-$ man du
-`
-
-Extended Access Control List (ACL)
-----------------------------------
-
-Extended ACLs provide another security mechanism beside the standard
-POSIX ACLs which are defined by three entries (for
-owner/group/others). Extended ACLs have more than the three basic
-entries. In addition, they also contain a mask entry and may contain any
-number of named user and named group entries.
-
-ACLs on a Lustre file system work exactly like ACLs on any Linux file
-system. They are manipulated with the standard tools in the standard
-manner. Below, we create a directory and allow a specific user access.
-
-`
-[vop999@login1.salomon ~]$ umask 027
-[vop999@login1.salomon ~]$ mkdir test
-[vop999@login1.salomon ~]$ ls -ld test
-drwxr-x--- 2 vop999 vop999 4096 Nov 5 14:17 test
-[vop999@login1.salomon ~]$ getfacl test
-# file: test
-# owner: vop999
-# group: vop999
-user::rwx
-group::r-x
-other::---
-
-[vop999@login1.salomon ~]$ setfacl -m user:johnsm:rwx test
-[vop999@login1.salomon ~]$ ls -ld test
-drwxrwx---+ 2 vop999 vop999 4096 Nov 5 14:17 test
-[vop999@login1.salomon ~]$ getfacl test
-# file: test
-# owner: vop999
-# group: vop999
-user::rwx
-user:johnsm:rwx
-group::r-x
-mask::rwx
-other::---
-`
-
-Default ACL mechanism can be used to replace setuid/setgid permissions
-on directories. Setting a default ACL on a directory (-d flag to
-setfacl) will cause the ACL permissions to be inherited by any newly
-created file or subdirectory within the directory. Refer to this page
-for more information on Linux ACL:
-
-[http://www.vanemery.com/Linux/ACL/POSIX_ACL_on_Linux.html ](http://www.vanemery.com/Linux/ACL/POSIX_ACL_on_Linux.html)
-
-Shared Workspaces
----------------------
-
-###HOME
-
-Users home directories /home/username reside on HOME filesystem.
-Accessible capacity is 0.5PB, shared among all users. Individual users
-are restricted by filesystem usage quotas, set to 250GB per user.
-If 250GB should prove as insufficient for particular user, please
-contact [support](https://support.it4i.cz/rt),
-the quota may be lifted upon request.
-
-The HOME filesystem is intended for preparation, evaluation, processing
-and storage of data generated by active Projects.
-
-The HOMEÂ should not be used to archive data of past Projects or other
-unrelated data.
-
-The files on HOME will not be deleted until end of the [users
-lifecycle](../../get-started-with-it4innovations/obtaining-login-credentials/obtaining-login-credentials.html).
-
-The workspace is backed up, such that it can be restored in case ofÂ
-catasthropic failure resulting in significant data loss. This backup
-however is not intended to restore old versions of user data or to
-restore (accidentaly) deleted files.
-
-HOME workspace
-Accesspoint
-/home/username
-Capacity
-0.5PB
-Throughput
-6GB/s
-User quota
-250GB
-Protocol
-NFS, 2-Tier
-### WORK
-
-The WORK workspace resides on SCRATCH filesystem. Users may create
-subdirectories and files in directories **/scratch/work/user/username**
-and **/scratch/work/project/projectid. **The /scratch/work/user/username
-is private to user, much like the home directory. The
-/scratch/work/project/projectid is accessible to all users involved in
-project projectid. >
-
-The WORK workspace is intended to store users project data as well as
-for high performance access to input and output files. All project data
-should be removed once the project is finished. The data on the WORK
-workspace are not backed up.
-
-Files on the WORK filesystem are **persistent** (not automatically
-deleted) throughout duration of the project.
-
-The WORK workspace is hosted on SCRATCH filesystem. The SCRATCH is
-realized as Lustre parallel filesystem and is available from all login
-and computational nodes. Default stripe size is 1MB, stripe count is 1.
-There are 54 OSTs dedicated for the SCRATCH filesystem.
-
-Setting stripe size and stripe count correctly for your needs may
-significantly impact the I/O performance you experience.
-
-WORK workspace
-Accesspoints
-/scratch/work/user/username
-/scratch/work/user/projectid
-Capacity
-1.6P
-Throughput
-30GB/s
-User quota
-100TB
-Default stripe size
-1MB
-Default stripe count
-1
-Number of OSTs
-54
-Protocol
-Lustre
-### TEMP
-
-The TEMP workspace resides on SCRATCH filesystem. The TEMP workspace
-accesspoint is /scratch/temp. Users may freely create subdirectories
-and files on the workspace. Accessible capacity is 1.6P, shared among
-all users on TEMP and WORK. Individual users are restricted by
-filesystem usage quotas, set to 100TB per user. The purpose of this
-quota is to prevent runaway programs from filling the entire filesystem
-and deny service to other users. >If 100TB should prove as
-insufficient for particular user, please contact
-[support](https://support.it4i.cz/rt), the quota may be
-lifted upon request.Â
-
-The TEMP workspace is intended for temporary scratch data generated
-during the calculation as well as for high performance access to input
-and output files. All I/O intensive jobs must use the TEMP workspace as
-their working directory.
-
-Users are advised to save the necessary data from the TEMP workspace to
-HOME or WORK after the calculations and clean up the scratch files.
-
-Files on the TEMP filesystem that are **not accessed for more than 90
-days** will be automatically **deleted**.
-
-The TEMP workspace is hosted on SCRATCH filesystem. The SCRATCH is
-realized as Lustre parallel filesystem and is available from all login
-and computational nodes. Default stripe size is 1MB, stripe count is 1.
-There are 54 OSTs dedicated for the SCRATCH filesystem.
-
-Setting stripe size and stripe count correctly for your needs may
-significantly impact the I/O performance you experience.
-
-TEMP workspace
-Accesspoint
-/scratch/temp
-Capacity
-1.6P
-Throughput
-30GB/s
-User quota
-100TB
-Default stripe size
-1MB
-Default stripe count
-1
-Number of OSTs
-54
-Protocol
-Lustre
-Â
-
-RAM disk
---------
-
-Every computational node is equipped with filesystem realized in memory,
-so called RAM disk.
-
-Use RAM disk in case you need really fast access to your data of limited
-size during your calculation.
-Be very careful, use of RAM disk filesystem is at the expense of
-operational memory.
-
-The local RAM disk is mounted as /ramdisk and is accessible to user
-at /ramdisk/$PBS_JOBID directory.
-
-The local RAM disk filesystem is intended for temporary scratch data
-generated during the calculation as well as for high performance access
-to input and output files. Size of RAM disk filesystem is limited. Be
-very careful, use of RAM disk filesystem is at the expense of
-operational memory. It is not recommended to allocate large amount of
-memory and use large amount of data in RAM disk filesystem at the same
-time.
-
-The local RAM disk directory /ramdisk/$PBS_JOBID will be deleted
-immediately after the calculation end. Users should take care to save
-the output data from within the jobscript.
-
-RAM disk
-Mountpoint
- /ramdisk
-Accesspoint
- /ramdisk/$PBS_JOBID
-Capacity
-120 GB
-Throughput
-over 1.5 GB/s write, over 5 GB/s read, single thread
-over 10 GB/s write, over 50 GB/s read, 16 threads
-
-User quota
-none
-Â
-
-Summary
-
-----------
-
- --------------------------------------------------
- |Mountpoint|Usage|Protocol|Net|Capacity|Throughput|Limitations|Access|
- ---------------------------------------- |---|---|---------------------- ------------- -------------- ------------ ------------- ---------- |**Version**|**Module**|------
- | /home|home directory|NFS, 2-Tier|0.5 PB|6 GB/s|Quota 250GB|Compute and login nodes|backed up|
-
- |/scratch/work|large project files|Lustre|1.69 PB|30 GB/s|Quota|Compute and login nodes|none|
-
-
- |/scratch/temp|job temporary data|Lustre|1.69 PB|30 GB/s|Quota 100TB|Compute and login nodes|files older 90 days removed|
-
- |/ramdisk|job temporary data, node local|local|120GB|90 GB/s|none|Compute nodes|purged after job ends|
- --------------------------------------------------
-
-Â
-
diff --git a/converted/docs.it4i.cz/salomon/storage/storage.pdf b/converted/docs.it4i.cz/salomon/storage/storage.pdf
deleted file mode 100644
index 1d00b5eea5a8c5e13d5c67b75f3419fddb38b18b..0000000000000000000000000000000000000000
Binary files a/converted/docs.it4i.cz/salomon/storage/storage.pdf and /dev/null differ
diff --git a/html_md.sh b/html_md.sh
index 0dd90261a9acc54f5fa8e229aa61c25326096747..04bd79a44326dd5c922632ef773eba47c39dcbec 100755
--- a/html_md.sh
+++ b/html_md.sh
@@ -2,7 +2,7 @@
### DOWNLOAD AND CONVERT DOCUMENTATION
# autor: kru0052
-# version: 0.52
+# version: 0.53
# change: repait bugs
# bugs: bad internal links
###
@@ -13,6 +13,7 @@ if [ "$1" = "-e" ]; then
counter=1
count=$(find ./converted -name "*.md" -type f | wc -l)
if [ "$2" = "epub" ]; then
+ rm -rf ./epub
find ./converted -name "*.md" |
while read i;
do
@@ -26,8 +27,21 @@ if [ "$1" = "-e" ]; then
pandoc ${a%.*}.md -o ${a%.*}.epub
cd "$c"
done
+
+ mkdir epub;
+ (while read i;
+ do
+ mkdir "./epub/$i";
+ done) < ./source/list_folder
+
+ # move epub files to new folders
+ echo "$(tput setaf 11)moved epub files$(tput setaf 15)";
+ while read a ; do
+ mv "./converted/${a%.*}.epub" "./epub/${a%.*}.epub";
+ done < ./source/list_md_mv
fi
if [ "$2" = "pdf" ]; then
+ rm -rf ./pdf
find ./converted -name "*.md" |
while read i;
do
@@ -61,17 +75,55 @@ if [ "$1" = "-e" ]; then
fi
cd "$c"
done
+ mkdir pdf;
+ (while read i;
+ do
+ mkdir "./pdf/$i";
+ done) < ./source/list_folder
+
+ # move epub files to new folders
+ echo "$(tput setaf 11)moved epub files$(tput setaf 15)";
+ while read a ; do
+ mv "./converted/${a%.*}.pdf" "./pdf/${a%.*}.pdf";
+ done < ./source/list_md_mv
fi
ENDTIME=$(date +%s)
echo "It takes $(($ENDTIME - $STARTTIME)) seconds to complete this task..."
fi
if [ "$1" = "-d" ]; then
- find . -name "*."$2 |
- while read i;
- do
- echo "$(tput setaf 9)$i deleted$(tput setaf 15)"
- rm "$i"
- done
+ if [ "$2" = "pdf" ]; then
+ echo "$(tput setaf 9)*.pdf deleted$(tput setaf 15)"
+ if [ -d ./pdf ]; then
+ rm -rf ./pdf
+ fi
+ elif [ "$2" = "epub" ]; then
+ echo "$(tput setaf 9)*.epub deleted$(tput setaf 15)"
+ if [ -d ./epub ]; then
+ rm -rf ./epub
+ fi
+ elif [ "$2" = "md" ]; then
+ echo "$(tput setaf 9)*.md deleted$(tput setaf 15)"
+ if [ -d ./converted ]; then
+ rm -rf ./converted
+ fi
+ elif [ "$2" = "all" ]; then
+ echo "$(tput setaf 9)all files deleted$(tput setaf 15)"
+ if [ -d ./converted ]; then
+ rm -rf ./converted
+ fi
+ if [ -d ./epub ]; then
+ rm -rf ./epub
+ fi
+ if [ -d ./pdf ]; then
+ rm -rf ./pdf
+ fi
+ if [ -d ./info ]; then
+ rm -rf ./info
+ fi
+ if [ -d ./docs.it4i.cz ]; then
+ rm -rf ./docs.it4i.cz
+ fi
+ fi
fi
if [ "$1" = "-w" ]; then
# download html pages