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# OpenFOAM
OpenFOAM is a free, open source CFD software package.
## Introduction
[OpenFOAM][a] is a free, open source CFD software package developed by [OpenCFD Ltd][b] at [ESI Group][c] and distributed by the [OpenFOAM Foundation][d]. It has a large user base across most areas of engineering and science from both commercial and academic organizations.
### Installed Version
Currently, several versions are available compiled by GCC/ICC compilers in single/double precision with several versions of OpenMPI.
The naming convention of the installed versions is:
`openfoam/<VERSION>-<COMPILER>-<openmpiVERSION>-<PRECISION>`
* `VERSION` - version of openfoam
* `COMPILER` - version of used compiler
* `openmpiVERSION` - version of used openmpi/impi
* `PRECISION` - DP/SP – double/single precision
Example of the available OpenFOAM modules syntax is `openfoam/2.2.1-icc-openmpi1.6.5-DP`
This means OpenFOAM version 2.2.1 compiled by the ICC compiler with openmpi1.6.5 in double precision.
### Available OpenFOAM Modules
To check the available modules, use:
```console
$ ml av
```
In /opt/modules/modulefiles/engineering, you can see the installed engineering softwares:
```console
------------------------------------ /opt/modules/modulefiles/engineering -------------------------------------------------------------
ansys/14.5.x matlab/R2013a-COM openfoam/2.2.1-icc-impi4.1.1.036-DP
comsol/43b-COM matlab/R2013a-EDU openfoam/2.2.1-icc-openmpi1.6.5-DP
comsol/43b-EDU openfoam/2.2.1-gcc481-openmpi1.6.5-DP 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 on how to use modules, look [here][1].
## Getting Started
To create OpenFOAM environment, use the commands:
```console
$ ml openfoam
$ source $FOAM_BASHRC
```
!!! note
Load the 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:
```console
$ mkdir -p $FOAM_RUN
```
The project directory is now available by typing:
```console
$ cd /home/<USER>/OpenFOAM/<USER>-<OFversion>/run
```
`<OFversion>` - for example `2.2.1`
or
```console
$ cd $FOAM_RUN
```
Copy the tutorial examples directory in the OpenFOAM distribution to the run directory:
```console
$ 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 test.sh Bash script:
```bash
#!/bin/bash
ml 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 (example for Karolina):
```console
$ sbatch -A PROJECT_ID -p qcpu --nodes=1 --ntasks=128 --time=03:00:00 test.sh
```
For information about job submission, look [here][2].
## Running Applications in Parallel
Run the second case, for example external incompressible turbulent flow - case - motorBike.
First we must run the serial application bockMesh and decomposePar for preparation of parallel computation.
!!! note
Create a test.sh Bash scrip:
```bash
#!/bin/bash
ml 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
```console
$ sbatch -A PROJECT_ID -p qcpu --nodes=1 --ntasks=16 --time=03:00:00 test.sh
```
This job creates a simple block mesh and domain decomposition. Check your decomposition and submit parallel computation:
!!! note
Create a testParallel.slurm script:
```bash
#!/bin/bash
#SBATCH --job-name=motorBike
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=16
#SBATCH --time=01:00:00
#SBATCH --partition=qcpu
#SBATCH --account=ACCOUNT_ID
ml openfoam/2.2.1-icc-openmpi1.6.5-DP
source $FOAM_BASHRC
cd $FOAM_RUN/tutorials/incompressible/simpleFoam/motorBike
nproc = 32
mpirun -hostfile ${$SLURM_JOB_NODELIST} -np $nproc snappyHexMesh -overwrite -parallel | tee snappyHexMesh.log
mpirun -hostfile ${$SLURM_JOB_NODELIST} -np $nproc potentialFoam -noFunctionObject-writep -parallel | tee potentialFoam.log
mpirun -hostfile ${$SLURM_JOB_NODELIST} -np $nproc simpleFoam -parallel | tee simpleFoam.log
```
`nproc` – the number of subdomains
Job submission
```console
$ srun testParallel.slurm
```
## Compile Your Own Solver
Initialize the OpenFOAM environment before compiling your solver:
```console
$ ml openfoam/2.2.1-icc-openmpi1.6.5-DP
$ source $FOAM_BASHRC
$ cd $FOAM_RUN/
```
Create the applications/solvers directory in the user directory:
```console
$ mkdir -p applications/solvers
$ cd applications/solvers
```
Copy icoFoam solver’s source files:
```console
$ cp -r $FOAM_SOLVERS/incompressible/icoFoam/My_icoFoam
$ cd My_icoFoam
```
Rename icoFoam.C to My_icoFOAM.C:
```console
$ mv icoFoam.C My_icoFoam.C
```
Edit the _files_ file in the _Make_ directory:
```bash
icoFoam.C
EXE = $(FOAM_APPBIN)/icoFoam
```
and change to:
```bash
My_icoFoam.C
EXE = $(FOAM_USER_APPBIN)/My_icoFoam
```
In the My_icoFoam directory, use the compilation command:
```console
$ wmake
```
[1]: ../../environment-and-modules.md
[2]: ../../general/job-submission-and-execution.md
[a]: http://www.openfoam.com/
[b]: http://www.openfoam.com/about
[c]: http://www.esi-group.com/
[d]: http://www.openfoam.org/
# Ovito
[OVITO][a] is a visualization and analysis software for output data generated in molecular dynamics, atomistic Monte-Carlo and other particle-based simulations.
## Introduction
A central aim of OVITO as a data visualization and analysis solution is to provide a maximum of flexibility – and, at the same time, ease-of-use – to support you in extracting meaningful information from simulation outputs. To achieve this goal, OVITO is based on a powerful data pipeline technology depicted below. The software offers a wide range of basic visualization and analysis functions (building blocks), which you can assemble to a sequence of processing steps. These operations, which are all configurable, will then be applied to the simulation data in real-time by the software. The outcome of the data pipeline is finally displayed on screen, may be rendered to an output image for publication, or exported to output data files.
![](../../img/ovito_data_pipeline.png)
The dynamic pipeline concept makes it possible for you to change and adjust the sequence of applied operations at any time. In other words, OVITO enables you to work in a non-destructive manner with a dataset as all manipulation steps can easily be revoked. You can adjust the order of filter and manipulation operations, their parameters, and also the input data itself –the program will re-evaluate the pipeline and update the display in real-time. Thanks to intelligent data caching and parallelization techniques implemented in OVITO, this all happens without interrupting your workflow.
For more information, see the [User Manual][b]
### Installed Version
For the list of installed versions, use:
```console
$ ml av ovito
```
## Getting Started
The visualization software requires [graphical user interface][1] or [virtual network computing][2] software.
[1]: ../../general/accessing-the-clusters/graphical-user-interface/x-window-system.md
[2]: ../../general/accessing-the-clusters/graphical-user-interface/vnc.md
[a]: https://www.ovito.org/
[b]: https://www.ovito.org/docs/current/
# ParaView
An open-source, multi-platform data analysis and visualization application.
## Introduction
[ParaView][a] 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.
## Installed Version
Currently, version 5.1.2 compiled with Intel/2017a against the Intel MPI library and OSMesa 12.0.2 is installed on the clusters.
## Usage
On the clusters, ParaView is to be used in the client-server mode. A parallel ParaView server is launched on compute nodes by the user and the client is launched on your desktop PC to control and view the visualization. Download the ParaView client application for your OS [here][b].
!!!Warning
Your version must match the version number installed on the cluster.
### Launching Server
To launch the server, you must first allocate compute nodes, for example:
```console
$ salloc -p qprod -A PROJECT_ID --nodes=2 --time=01:00:00 --x11
```
to launch an interactive session on 2 nodes. For details, refer to [Job Submission and Execution][1] section.
After the interactive session is opened, load the ParaView module:
```console
$ ml ParaView/5.1.2-intel-2017a-mpi
```
Now launch the parallel server, with the number of processes equal to the number of nodes times 24:
```console
$ mpirun -np 48 pvserver --use-offscreen-rendering
Waiting for client...
Connection URL: cs://r37u29n1006:11111
Accepting connection(s): r37u29n1006:11111i
```
Note that in this case, the server is listening on the compute node r37u29n1006, we will use this information later.
### Client Connection
Because a direct connection is not allowed to compute nodes on clusters, you must establish an 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 establish an SSH tunnel:
```console
Barbora: $ ssh -TN -L 12345:r37u29n1006:11111 username@barbora.it4i.cz
```
Replace username with your login and r37u29n1006 with the name of the compute node on which your ParaView server is running (see the previous step).
If you use PuTTY on Windows, load Barbora connection configuration, then go to *Connection* -> *SSH* -> *Tunnels* to set up the port forwarding.
Fill the Source port and Destination fields. **Do not forget to click the Add button.**
![](../../img/paraview_ssh_tunnel_salomon.png "SSH Tunnel in PuTTY")
Now launch ParaView client installed on your desktop PC. Select *File* -> *Connect...* and fill in the following :
![](../../img/paraview_connect_salomon.png "ParaView - Connect to server")
The configuration is now saved for later use. Now click Connect to connect to the ParaView server. In your terminal where you have the interactive session with the ParaView server launched, you should see:
```console
Client connected.
```
You can now use Parallel ParaView.
### Close Server
Remember to close the interactive session after you finish working with the ParaView server, as it will remain launched and continue consuming resources even after your client is disconnected.
[1]: ../../general/job-submission-and-execution.md
[a]: http://www.paraview.org/
[b]: http://paraview.org/paraview/resources/software.php
# QtiPlot
A cross-platform scientific application for data analysis and visualisation.
## Introduction
[QtiPlot][1] is a cross-platform scientific application for data analysis and visualisation.
QtiPlot can be used to present 2D and 3D data and has various data analysis functions like curve fitting.
The program is also extensible to a considerable degree via muParser and Python scripting language,
which allows adding the arbitrary user-defined functions with access to graphs, matrices and data tables.
## Installed Versions
For the current list of installed versions, use:
```console
$ ml av QtiPlot
```
## Running QtiPlot
!!! important
You must first enable the [VNC][a] or [X Window System][b] GUI environment.
To run QtiPlot, use:
```console
$ ml QtiPlot; qtiplot
```
[1]: https://www.qtiplot.com/index.html
[a]: ../../../general/accessing-the-clusters/graphical-user-interface/vnc/
[b]: ../../../general/accessing-the-clusters/graphical-user-interface/x-window-system/
# Vesta
## Introduction
VESTA (Visualization for Electronic and Structural Analysis) is a 3D visualization program for structural models, volumetric data such as electron/nuclear densities, and crystal morphologies. Some of the novel features of VESTA include:
* Deal with multiple structural models, volumetric data, and crystal morphologies in the same window;
* Support multiple tabs corresponding to files;
* Support multiple windows with more than two tabs in the same process;
* Deal with virtually unlimited number of objects such as atoms, bonds polyhedra, and polygons on isosurfaces (theoretical limit on 32bit operating system is 1,073,741,823);
* Support lattice transformation from conventional to non-conventional lattice by using matrix. The transformation matrix is also used to create superlattice and sublattice;
* Visualize interatomic distances and bond angles that are restrained in Rietveld analysis with RIETAN-FP;
* Transparent isosurfaces can be overlap with structural models;
* Isosurface can be colored on the basis of another physical quantity;
* Arithmetic operations among multiple volumetric data files;
* High quality smooth rendering of isosurfaces and sections;
* Export high-resolution graphic images exceeding Video card limitation.
For the full list of features and supported file formats, see the [official page][1].
## Manual
The VESTA manual can be found [here][2].
## Installed Versions
For the current list of installed versions, use:
```console
$ ml av vesta
```
!!!note Module Availability
This module is currently availble on the Barbora cluster only.
[1]: https://jp-minerals.org/vesta/en/
[2]: https://jp-minerals.org/vesta/archives/VESTA_Manual.pdf
# VirtualGL
VirtualGL is an open source program that redirects the 3D rendering commands from Unix and Linux OpenGL applications to 3D accelerator hardware in a dedicated server and displays the rendered output interactively to a thin client located elsewhere on the network.
See the documentation [here][a].
!!! info
VirtualGL is available on Barbora and Karolina.
## How to Use
**Run VNC**
* VNC Server
Read our documentation on [VNC server][1].
```console
Warning: No xauth data; using fake authentication data for X11 forwarding.
Last login: Tue Mar 3 14:20:18 2020 from vpn-kru0052.it4i.cz
____ _
| _ \ | |
| |_) | __ _ _ __| |__ ___ _ __ __ _
| _ < / _` | '__| '_ \ / _ \| '__/ _` |
| |_) | (_| | | | |_) | (_) | | | (_| |
|____/ \__,_|_| |_.__/ \___/|_| \__,_|
...running on Red Hat Enterprise Linux 7.x
kru0052@login1:~$ vncserver :99
New 'login1.barbora.it4i.cz:99 (kru0052)' desktop is login1.barbora.it4i.cz:99
Starting applications specified in /home/kru0052/.vnc/xstartup
Log file is /home/kru0052/.vnc/login1.barbora.it4i.cz:99.log
kru0052@login1:~$
```
* VNC Client (your local machine)
```console
root@toshiba:~# ssh -L 5999:localhost:5999 kru0052@login1.barbora.it4i.cz -X
```
* Connect to a VNC server from a VNC client (your local machine)
```console
vncviewer localhost:99
```
Or via GUI.
![](../../img/vnc.jpg)
!!! tip
To resize the window scale, use the `xrandr -s 1920x1200` command.
**Run vglclient on the login server (use the terminal in the local machine VNC window)**
```console
kru0052@login1:~$ ml VirtualGL
kru0052@login1:~$ vglclient
VirtualGL Client 64-bit v2.6.1 (Build 20200228)
Listening for unencrypted connections on port 4242
```
![](../../img/client.jpg)
**Execute an interactive job on vizserv (use another terminal in the local machine VNC window)**
```console
[kru0052@login1.barbora ~]$ salloc -p qviz -A PROJECT_ID --x11
salloc: Granted job allocation 694741
salloc: Waiting for resource configuration
salloc: Nodes vizserv1 are ready for job
[kru0052@vizserv1.barbora ~]$
```
![](../../img/job.jpg)
**New SSH connection on vizserv - elimination of the Slurm setting (use another terminal in the local machine VNC window)**
```console
kru0052@login1:~$ ssh vizserv1 -X
Last login: Tue Mar 3 13:54:33 2020 from login1.barbora.it4i.cz
kru0052@vizserv1:~$
```
![](../../img/ssh.jpg)
**Run the graphical application**
```console
kru0052@vizserv1:~$ vglrun /apps/easybuild/glxgears
[VGL] NOTICE: Automatically setting VGL_CLIENT environment variable to
[VGL] 10.32.2.1, the IP address of your SSH client.
libGL error: unable to load driver: swrast_dri.so
libGL error: failed to load driver: swrast
17128 frames in 5.0 seconds = 3425.573 FPS
17251 frames in 5.0 seconds = 3450.128 FPS
...
```
![](../../img/glxgears.jpg)
## Examples for Remote Visualization on Barbora Cluster
<div align="center">
<iframe width="854" height="480" src="https://www.youtube.com/embed/BArIbIC_-24"></iframe>
</div>
[1]: ../../../general/accessing-the-clusters/graphical-user-interface/vnc/
[a]: https://www.virtualgl.org/
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<body>
<div id="content">
<div class="header">
<div class="logo"><img src="http://content.allinea.com/downloads/performance-report-logo.png" alt="Allinea Performance Reports" /></div>
<div class="header_left">
<div class="application_details">
<table>
<tr><td class="details_key">Executable:</td><td id="exe_name">mympiprog.x</td></tr>
<tr><td class="details_key">Resources:</td><td id="num_procs">32 processes, 2 nodes</td></tr>
<tr><td class="details_key">Machine:</td><td id="machine_name">cn182</td></tr>
<tr><td class="details_key">Start time:</td><td id="start_date">Wed Oct 15 16:56:23 2014</td></tr>
<tr><td class="details_key">Total time:</td><td id="time_string">7 seconds (0 minutes)</td></tr>
<tr><td class="details_key">Full path:</td><td id="exe_path">/home/user</td></tr>
<tr><td class="details_key">Notes:</td><td id="notes"></td></tr>
</table>
</div>
</div>
<div id="time_radar"></div>
<div class="clear"></div>
</div>
<hr />
<div id="error">
<p><strong>Error: javascript is not running</strong></p>
<p>The graphs in this Performance Report require <strong>javascript</strong>, which is disabled or not working.</p>
<p>Check whether your javascript support is enabled or try another browser.<p>
<p>Remember, you can always contact <a href="mailto:support@allinea.com">support@allinea.com</a>, we're very nice!</p>
</div>
<div class="summary">
<div class="heading">Summary: mympiprog.x is <span class="cpu_span">CPU-bound</span> in this configuration</div>
<div>The total wallclock time was spent as follows:</div>
<table class="summary_table">
<tr><td class="heading_cpu">CPU</td><td class="percent">88.6%</td><td class="bar_graph"><div id="cpu_bar" /></td>
<td class="details"><p>Time spent running application code. High values are usually good.</p><p>This is <span id="summary_cpu_class">high</span>; check the CPU performance section for optimization advice.</p></td></tr>
<tr><td class="heading_mpi">MPI</td><td class="percent">11.4%</td><td class="bar_graph"><div id="mpi_bar" /></td>
<td class="details"><p>Time spent in MPI calls. High values are usually bad.</p><p>This is <span id="summary_mpi_class">very low</span>; this code may benefit from increasing the process count.</p></td></tr>
<tr><td class="heading_io">I/O</td><td class="percent">0.0%</td><td class="bar_graph"><div id="io_bar" /></td>
<td class="details"><p>Time spent in filesystem I/O. High values are usually bad.</p><p>This is <span id="summary_io_class">negligible</span>; there's no need to investigate I/O performance.</p></td></tr>
</table>
<div class="overview_general_advice"><p>This application run was <span class="cpu_span">CPU-bound</span>. A breakdown of this time and advice for investigating further is in the <span class="cpu_span">CPU</span> section below.</p><p id="overview_advice">As very little time is spent in MPI calls, this code may also benefit from running at larger scales.</p></div>
</div>
<hr />
<div class="subsections">
<div class="ltcol">
<div class="heading_cpu">CPU</div>
<div>A breakdown of how the <span class="cpu_span">88.6</span>% total CPU time was spent:</div>
<table id="cpu_chart">
<tr><td>Scalar numeric ops</td><td class="right_cell"><span class="cpu_num_span">50.0</span>%</td><td class="mini_bar_graph"><div id="cpu_num_bar" /></td></tr>
<tr><td>Vector numeric ops</td><td class="right_cell"><span class="cpu_vec_span">50.0</span>%</td><td class="mini_bar_graph"><div id="cpu_vec_bar" /></td></tr>
<tr><td>Memory accesses</td><td class="right_cell"><span class="cpu_mem_span">0.0</span>%</td><td class="mini_bar_graph"><div id="cpu_mem_bar" /></td></tr>
<tr><td>Other</td><td class="right_cell"><span class="cpu_other_span">0.0</span>%</td><td class="mini_bar_graph"><div id="cpu_other_bar" /></td></tr>
</table>
<div id="cpu_explanation">
<div class="explanation">The per-core performance is arithmetic-bound. Try to increase the amount of time spent in vectorized instructions by analyzing the compiler's vectorization reports.</div>
<div class="explanation"></div>
</div>
</div>
<div class="rtcol">
<div class="heading_mpi">MPI</div>
<div>Of the <span class="mpi_span">11.4</span>% total time spent in MPI calls:</div>
<table id="mpi_chart">
<tr><td>Time in collective calls</td><td class="right_cell"><span class="mpi_col_span">100.0</span>%</td><td class="mini_bar_graph"><div id="mpi_col_bar" /></td></tr>
<tr><td>Time in point-to-point calls</td><td class="right_cell"><span class="mpi_p2p_span">0.0</span>%</td><td class="mini_bar_graph"><div id="mpi_p2p_bar" /></td></tr>
<tr><td>Effective process collective rate</td><td class="right_cell"><span class="mpi_colrate_span"><span id="mpi_colrate_num">1.65e+02</span></span>&nbsp;<span id="mpi_colrate_units"></span></td><td class="mini_bar_graph"><div id="mpi_colrate_bar" /></td></tr>
<tr><td>Effective process point-to-point rate</td><td class="right_cell"><span class="mpi_p2prate_span"><span id="mpi_p2prate_num">0.00e+00</span></span>&nbsp;<span id="mpi_p2prate_units"></span></td><td class="mini_bar_graph"><div id="mpi_p2prate_bar" /></td></tr>
</table>
<div id="mpi_explanation">
<div class="explanation">Most of the time is spent in collective calls with a very low transfer rate. This suggests load imbalance is causing synchonization overhead; use an MPI profiler to investigate further.</div>
<div class="explanation"></div>
</div>
</div>
<div class="clear"></div>
</div>
<div class="subsections">
<div class="ltcol">
<div class="heading_io">I/O</div>
<div>A breakdown of how the <span class="io_span">0.0</span>% total I/O time was spent:</div>
<table id="io_chart">
<tr><td>Time in reads</td><td class="right_cell"><span class="io_read_span">0.0</span>%</td><td class="mini_bar_graph"><div id="io_read_bar" /></td></tr>
<tr><td>Time in writes</td><td class="right_cell"><span class="io_write_span">0.0</span>%</td><td class="mini_bar_graph"><div id="io_write_bar" /></td></tr>
<tr><td>Effective process read rate</td><td class="right_cell"><span class="io_readrate_span"><span id="io_readrate_num">0.00e+00</span></span>&nbsp;<span id="io_readrate_units"></span></td><td class="mini_bar_graph"><div id="io_readrate_bar" /></td></tr>
<tr><td>Effective process write rate</td><td class="right_cell"><span class="io_writerate_span"><span id="io_writerate_num">0.00e+00</span></span>&nbsp;<span id="io_writerate_units"></span></td><td class="mini_bar_graph"><div id="io_writerate_bar" /></td></tr>
</table>
<div id="io_explanation">
<div class="explanation">No time is spent in I/O operations. There's nothing to optimize here!</div>
<div class="explanation"></div>
</div>
</div>
<div class="rtcol">
<div class="heading_ram">Memory</div>
<div>Per-process memory usage may also affect scaling:</div>
<table id="ram_chart">
<tr><td>Mean process memory usage</td><td class="right_cell"><span class="ram_mean_span"><span id="ram_mean_num">2.33e+07</span></span>&nbsp;<span id="ram_mean_units"></span></td><td class="mini_bar_graph"><div id="ram_mean_bar" /></td></tr>
<tr><td>Peak process memory usage</td><td class="right_cell"><span class="ram_peak_span"><span id="ram_peak_num">2.35e+07</span></span>&nbsp;<span id="ram_peak_units"></span></td><td class="mini_bar_graph"><div id="ram_peak_bar" /></td></tr>
<tr><td>Peak node memory usage</td><td class="right_cell"><span class="ram_node_span">2.8</span>%</td><td class="mini_bar_graph"><div id="ram_node_bar" /></td></tr>
</table>
<div id="ram_explanation">
<div class="explanation">The peak node memory usage is very low. You may be able to reduce the amount of allocation time used by running with fewer MPI processes and more data on each process.</div>
<div class="explanation"></div>
</div>
</div>
<div class="clear"></div>
</div>
</div> <!-- content -->
</body>
</html>
Executable: mympiprog.x
Resources: 32 processes, 2 nodes
Machine: cn182
Started on: Wed Oct 15 16:56:23 2014
Total time: 7 seconds (0 minutes)
Full path: /home/user
Notes:
Summary: mympiprog.x is CPU-bound in this configuration
CPU: 88.6% |========|
MPI: 11.4% ||
I/O: 0.0% |
This application run was CPU-bound. A breakdown of this time and advice for investigating further is found in the CPU section below.
As very little time is spent in MPI calls, this code may also benefit from running at larger scales.
CPU:
A breakdown of how the 88.6% total CPU time was spent:
Scalar numeric ops: 50.0% |====|
Vector numeric ops: 50.0% |====|
Memory accesses: 0.0% |
Other: 0.0% |
The per-core performance is arithmetic-bound. Try to increase the amount of time spent in vectorized instructions by analyzing the compiler's vectorization reports.
MPI:
A breakdown of how the 11.4% total MPI time was spent:
Time in collective calls: 100.0% |=========|
Time in point-to-point calls: 0.0% |
Effective collective rate: 1.65e+02 bytes/s
Effective point-to-point rate: 0.00e+00 bytes/s
Most of the time is spent in collective calls with a very low transfer rate. This suggests load imbalance is causing synchonization overhead; use an MPI profiler to investigate further.
I/O:
A breakdown of how the 0.0% total I/O time was spent:
Time in reads: 0.0% |
Time in writes: 0.0% |
Effective read rate: 0.00e+00 bytes/s
Effective write rate: 0.00e+00 bytes/s
No time is spent in I/O operations. There's nothing to optimize here!
Memory:
Per-process memory usage may also affect scaling:
Mean process memory usage: 2.33e+07 bytes
Peak process memory usage: 2.35e+07 bytes
Peak node memory usage: 2.8% |
The peak node memory usage is very low. You may be able to reduce the amount of allocation time used by running with fewer MPI processes and more data on each process.
/*
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
/*
* Author of revised version: Franklyn Pinedo
*
* Adapted from Source Code in C of Tutorial/User's Guide for MPI by
* Peter Pacheco.
*/
/*
* Copyright (c) 2011 Cisco Systems, Inc. All rights reserved.
*
*/
import mpi.*;
class Hello {
static public void main(String[] args) throws MPIException {
MPI.Init(args);
int myrank = MPI.COMM_WORLD.getRank();
int size = MPI.COMM_WORLD.getSize() ;
System.out.println("Hello world from rank " + myrank + " of " + size);
MPI.Finalize();
}
}
/*
* Copyright (c) 2011 Cisco Systems, Inc. All rights reserved.
*
* Simple ring test program
*/
import mpi.* ;
class Ring {
static public void main(String[] args) throws MPIException {
MPI.Init(args) ;
int source; // Rank of sender
int dest; // Rank of receiver
int tag=50; // Tag for messages
int next;
int prev;
int message[] = new int [1];
int myrank = MPI.COMM_WORLD.getRank() ;
int size = MPI.COMM_WORLD.getSize() ;
/* Calculate the rank of the next process in the ring. Use the
modulus operator so that the last process "wraps around" to
rank zero. */
next = (myrank + 1) % size;
prev = (myrank + size - 1) % size;
/* If we are the "master" process (i.e., MPI_COMM_WORLD rank 0),
put the number of times to go around the ring in the
message. */
if (0 == myrank) {
message[0] = 10;
System.out.println("Process 0 sending " + message[0] + " to rank " + next + " (" + size + " processes in ring)");
MPI.COMM_WORLD.send(message, 1, MPI.INT, next, tag);
}
/* Pass the message around the ring. The exit mechanism works as
follows: the message (a positive integer) is passed around the
ring. Each time it passes rank 0, it is decremented. When
each processes receives a message containing a 0 value, it
passes the message on to the next process and then quits. By
passing the 0 message first, every process gets the 0 message
and can quit normally. */
while (true) {
MPI.COMM_WORLD.recv(message, 1, MPI.INT, prev, tag);
if (0 == myrank) {
--message[0];
System.out.println("Process 0 decremented value: " + message[0]);
}
MPI.COMM_WORLD.send(message, 1, MPI.INT, next, tag);
if (0 == message[0]) {
System.out.println("Process " + myrank + " exiting");
break;
}
}
/* The last process does one extra send to process 0, which needs
to be received before the program can exit */
if (0 == myrank) {
MPI.COMM_WORLD.recv(message, 1, MPI.INT, prev, tag);
}
MPI.Finalize();
}
}
/*
* Copyright (c) 2007 Sun Microsystems, Inc. All rights reserved.
*/
/*
* Test the connectivity between all processes.
*/
#include <errno.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <netdb.h>
#include <unistd.h>
#include <mpi.h>
int
main(int argc, char **argv)
{
MPI_Status status;
int verbose = 0;
int rank;
int np; /* number of processes in job */
int peer;
int i;
int j;
int length;
char name[MPI_MAX_PROCESSOR_NAME+1];
MPI_Init(&argc, &argv);
MPI_Comm_rank(MPI_COMM_WORLD, &rank);
MPI_Comm_size(MPI_COMM_WORLD, &np);
/*
* If we cannot get the name for whatever reason, just
* set it to unknown. */
if (MPI_SUCCESS != MPI_Get_processor_name(name, &length)) {
strcpy(name, "unknown");
}
if (argc>1 && strcmp(argv[1], "-v")==0)
verbose = 1;
for (i=0; i<np; i++) {
if (rank==i) {
/* rank i sends to and receives from each higher rank */
for(j=i+1; j<np; j++) {
if (verbose)
printf("checking connection between rank %d on %s and rank %-4d\n",
i, name, j);
MPI_Send(&rank, 1, MPI_INT, j, rank, MPI_COMM_WORLD);
MPI_Recv(&peer, 1, MPI_INT, j, j, MPI_COMM_WORLD, &status);
}
} else if (rank>i) {
/* receive from and reply to rank i */
MPI_Recv(&peer, 1, MPI_INT, i, i, MPI_COMM_WORLD, &status);
MPI_Send(&rank, 1, MPI_INT, i, rank, MPI_COMM_WORLD);
}
}
MPI_Barrier(MPI_COMM_WORLD);
if (rank==0)
printf("Connectivity test on %d processes PASSED.\n", np);
MPI_Finalize();
return 0;
}
/*
* Copyright (c) 2004-2006 The Trustees of Indiana University and Indiana
* University Research and Technology
* Corporation. All rights reserved.
* Copyright (c) 2006 Cisco Systems, Inc. All rights reserved.
*
* Sample MPI "hello world" application in C
*/
#include <stdio.h>
#include "mpi.h"
int main(int argc, char* argv[])
{
int rank, size, len;
char version[MPI_MAX_LIBRARY_VERSION_STRING];
MPI_Init(&argc, &argv);
MPI_Comm_rank(MPI_COMM_WORLD, &rank);
MPI_Comm_size(MPI_COMM_WORLD, &size);
MPI_Get_library_version(version, &len);
printf("Hello, world, I am %d of %d, (%s, %d)\n",
rank, size, version, len);
MPI_Finalize();
return 0;
}
//
// Copyright (c) 2004-2006 The Trustees of Indiana University and Indiana
// University Research and Technology
// Corporation. All rights reserved.
// Copyright (c) 2006 Cisco Systems, Inc. All rights reserved.
//
// Sample MPI "hello world" application in C++
//
// NOTE: The MPI C++ bindings were deprecated in MPI-2.2 and removed
// from the standard in MPI-3. Open MPI still provides C++ MPI
// bindings, but they are no longer built by default (and may be
// removed in a future version of Open MPI). You must
// --enable-mpi-cxx when configuring Open MPI to enable the MPI C++
// bindings.
//
#include "mpi.h"
#include <iostream>
int main(int argc, char **argv)
{
int rank, size, len;
char version[MPI_MAX_LIBRARY_VERSION_STRING];
MPI::Init();
rank = MPI::COMM_WORLD.Get_rank();
size = MPI::COMM_WORLD.Get_size();
MPI_Get_library_version(version, &len);
std::cout << "Hello, world! I am " << rank << " of " << size
<< "(" << version << ", " << len << ")" << std::endl;
MPI::Finalize();
return 0;
}
C
C Copyright (c) 2004-2006 The Trustees of Indiana University and Indiana
C University Research and Technology
C Corporation. All rights reserved.
C Copyright (c) 2006-2015 Cisco Systems, Inc. All rights reserved.
C $COPYRIGHT$
C
C Sample MPI "hello world" application using the Fortran mpif.h
C bindings.
C
program main
implicit none
include 'mpif.h'
integer ierr, rank, size, len
character(len=MPI_MAX_LIBRARY_VERSION_STRING) version
call MPI_INIT(ierr)
call MPI_COMM_RANK(MPI_COMM_WORLD, rank, ierr)
call MPI_COMM_SIZE(MPI_COMM_WORLD, size, ierr)
call MPI_GET_LIBRARY_VERSION(version, len, ierr)
write(*, '("Hello, world, I am ", i2, " of ", i2, ": ", a)')
& rank, size, version
call MPI_FINALIZE(ierr)
end