diff --git a/docs.it4i/software/debuggers/Introduction.md b/docs.it4i/software/debuggers/Introduction.md
new file mode 100644
index 0000000000000000000000000000000000000000..1e8157c460b14cce5540d832c1d71af67c2da7d5
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+++ b/docs.it4i/software/debuggers/Introduction.md
@@ -0,0 +1,62 @@
+# 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.
+
+```console
+$ ml intel
+$ idb
+```
+
+Read more at the [Intel Debugger](../../anselm/software/intel-suite/intel-debugger/) 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.
+
+```console
+$ ml Forge
+$ forge
+```
+
+Read more at the [Allinea DDT](allinea-ddt/) 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.
+
+```console
+$ ml PerformanceReports/6.0
+$ perf-report mpirun -n 64 ./my_application argument01 argument02
+```
+
+Read more at the [Allinea Performance Reports](allinea-performance-reports/) 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.
+
+```console
+$ ml TotalView/8.15.4-6-linux-x86-64
+$ totalview
+```
+
+Read more at the [Totalview](total-view/) page.
+
+## Vampir Trace Analyzer
+
+Vampir is a GUI trace analyzer for traces in OTF format.
+
+```console
+ $ ml Vampir/8.5.0
+ $ vampir
+```
+
+Read more at the [Vampir](vampir/) page.
diff --git a/docs.it4i/software/debuggers/aislinn.md b/docs.it4i/software/debuggers/aislinn.md
new file mode 100644
index 0000000000000000000000000000000000000000..2a945a04e8c74218b312c2ad9d6bdd08f1f54a5b
--- /dev/null
+++ b/docs.it4i/software/debuggers/aislinn.md
@@ -0,0 +1,102 @@
+# 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: <mailto:stanislav.bohm@vsb.cz>.
+
+## Usage
+
+Let us have the following program that contains a bug that is not manifested in all runs:
+
+```cpp
+#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:
+
+```console
+$ ml aislinn
+```
+
+Now we compile the program by Aislinn implementation of MPI. There are `mpicc` for C programs and `mpicxx` 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`.
+
+```console
+$ 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`.
+
+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
+
+```console
+$ 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.:
+
+```console
+$ 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.
diff --git a/docs.it4i/software/debuggers/allinea-ddt.md b/docs.it4i/software/debuggers/allinea-ddt.md
new file mode 100644
index 0000000000000000000000000000000000000000..67bfdff184ed1244a154848de728d58f4c678c94
--- /dev/null
+++ b/docs.it4i/software/debuggers/allinea-ddt.md
@@ -0,0 +1,94 @@
+# 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, or
+* 8 users can debug on single accelerator.
+
+## Compiling Code to Run With DDT
+
+### Modules
+
+Load all necessary modules to compile the code. For example:
+
+```console
+$ ml intel
+$ ml impi **or** ml OpenMPI/X.X.X-icc
+```
+
+Load the Allinea DDT module:
+
+```console
+$ ml Forge
+```
+
+Compile the code:
+
+```console
+$ 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:
+
+!!! note
+ \- **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 DDT
+
+Be sure to log in with an X window forwarding enabled. This could mean using the -X in the ssh:
+
+```console
+$ 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](/general/accessing-the-clusters/graphical-user-interface/x-window-system/)
+
+From the login node an interactive session **with X windows forwarding** (-X option) can be started by following command:
+
+```console
+$ 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 ddt command followed by the name of the executable to debug:
+
+```console
+$ 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.
+
+
+
+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".
+
+```console
+ddt -start -np 4 ./hello_debug_impi
+```
+
+## Documentation
+
+Users can find original User Guide after loading the DDT module:
+
+```console
+$DDTPATH/doc/userguide.pdf
+```
+
+[1] Discipline, Magic, Inspiration and Science: Best Practice Debugging with Allinea DDT, Workshop conducted at LLNL by Allinea on May 10, 2013, [link](https://computing.llnl.gov/tutorials/allineaDDT/index.html)
diff --git a/docs.it4i/software/debuggers/allinea-performance-reports.md b/docs.it4i/software/debuggers/allinea-performance-reports.md
new file mode 100644
index 0000000000000000000000000000000000000000..14451cdb0a25a5704fcafb61b9b0a558b6df882c
--- /dev/null
+++ b/docs.it4i/software/debuggers/allinea-performance-reports.md
@@ -0,0 +1,59 @@
+# Allinea Performance Reports
+
+## Introduction
+
+Allinea Performance Reports characterize the performance of HPC application runs. After executing your application through the tool, a synthetic HTML report is generated automatically, containing information about several metrics along with clear behavior statements and hints to help you improve the efficiency of your runs.
+
+The Allinea Performance Reports is most useful in profiling MPI rograms.
+
+Our license is limited to 64 MPI processes.
+
+## Modules
+
+Allinea Performance Reports version 6.0 is available
+
+```console
+$ ml PerformanceReports/6.0
+```
+
+The module sets up environment variables, required for using the Allinea Performance Reports.
+
+## Usage
+
+Use the the perf-report wrapper on your (MPI) program.
+
+Instead of [running your MPI program the usual way](../mpi/mpi/), use the the perf report wrapper:
+
+```console
+$ 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](../../anselm/job-submission-and-execution/).
+
+## 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:
+
+```console
+$ 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:
+
+```console
+$ ml intel
+$ ml PerfReports/6.0
+$ mpirun ./mympiprog.x
+```
+
+Now lets profile the code:
+
+```console
+$ 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.
diff --git a/docs.it4i/software/debuggers/cube.md b/docs.it4i/software/debuggers/cube.md
new file mode 100644
index 0000000000000000000000000000000000000000..4edf6ea02a445e633315ae4448c72e2c74a72fae
--- /dev/null
+++ b/docs.it4i/software/debuggers/cube.md
@@ -0,0 +1,36 @@
+# 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
+* **system 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](../../modules-matrix/):
+
+* cube/4.2.3-gcc, compiled with GCC
+* cube/4.2.3-icc, compiled with Intel compiler
+
+## Usage
+
+CUBE is a graphical application. Refer to Graphical User Interface documentation for a list of methods to launch graphical applications on Anselm.
+
+!!! note
+ Analyzing large data sets can consume large amount of CPU and RAM. Do not perform large analysis on login nodes.
+
+After loading the appropriate 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.
+
+References
+1\. <http://www.scalasca.org/software/cube-4.x/download.html>
diff --git a/docs.it4i/software/debuggers/intel-performance-counter-monitor.md b/docs.it4i/software/debuggers/intel-performance-counter-monitor.md
new file mode 100644
index 0000000000000000000000000000000000000000..3373cc4eeb9c92ab49f1ee9e72005d70911d1f46
--- /dev/null
+++ b/docs.it4i/software/debuggers/intel-performance-counter-monitor.md
@@ -0,0 +1,281 @@
+# 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/). 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](../../modules-matrix/) issue:
+
+```console
+$ ml intelpcm
+```
+
+## Command Line Tools
+
+PCM provides a set of tools to monitor system/or application.
+
+### Pcm-Memory
+
+ Measures memory bandwidth of your application or the whole system. Usage:
+
+```console
+$ 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:
+
+```console
+ ---------------------------------------||---------------------------------------
+ -- 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 --
+ ---------------------------------------||---------------------------------------
+```
+
+### 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: `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 :
+
+```console
+ $ 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
+```
+
+### Pcm-Sensor
+
+Can be used as a sensor for ksysguard GUI, which is currently not installed on Anselm.
+
+## 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.
+
+!!! note
+ 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 :
+
+```cpp
+ #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 :
+
+```console
+$ icc matrix.cpp -o matrix -lpthread -lpcm
+```
+
+Sample output:
+
+```console
+$ ./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>
+1. <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.
+1. <http://intel-pcm-api-documentation.github.io/classPCM.html> API Documentation
diff --git a/docs.it4i/software/debuggers/intel-vtune-amplifier.md b/docs.it4i/software/debuggers/intel-vtune-amplifier.md
new file mode 100644
index 0000000000000000000000000000000000000000..7da1fc0053cd8b7c9c62fe205b45397b083bc059
--- /dev/null
+++ b/docs.it4i/software/debuggers/intel-vtune-amplifier.md
@@ -0,0 +1,94 @@
+# 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:
+
+```console
+$ 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 :
+
+```console
+$ 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:
+
+```console
+$ module add VTune/2016_update1
+```
+
+and launch the GUI :
+
+```console
+$ 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. 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:
+
+```console
+/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.
+
+### 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 binaryand MIC card to run on.
+
+!!! note
+ 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:
+
+```console
+$ /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:
+
+```console
+$ /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.
+
+## References
+
+1. [Performance Tuning for Intel® Xeon Phi™ Coprocessors](https://www.rcac.purdue.edu/tutorials/phi/PerformanceTuningXeonPhi-Tullos.pdf)
+1. [Intel® VTune™ Amplifier Support](https://software.intel.com/en-us/intel-vtune-amplifier-xe-support/documentation)
+1. [https://software.intel.com/en-us/amplifier_help_linux](https://software.intel.com/en-us/amplifier_help_linux)
diff --git a/docs.it4i/software/debuggers/mympiprog_32p_2014-10-15_16-56.html b/docs.it4i/software/debuggers/mympiprog_32p_2014-10-15_16-56.html
new file mode 100644
index 0000000000000000000000000000000000000000..ce60070a9ee25a91973a577fd048d88f31d4680e
--- /dev/null
+++ b/docs.it4i/software/debuggers/mympiprog_32p_2014-10-15_16-56.html
@@ -0,0 +1,610 @@
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+ return getHorizontalPosition(i, cfg.w/2, (Math.max(j.value, 0)/cfg.maxValue)*cfg.factor);
+ })
+ .attr("cy", function(j, i){
+ return getVerticalPosition(i, cfg.h/2, (Math.max(j.value, 0)/cfg.maxValue)*cfg.factor);
+ })
+ .attr("data-id", function(j){return j.axis})
+ .style("fill", cfg.color(series)).style("fill-opacity", .9)
+ .on('mouseover', function (d){
+ newX = parseFloat(d3.select(this).attr('cx')) - 10;
+ newY = parseFloat(d3.select(this).attr('cy')) - 5;
+ tooltip.attr('x', newX).attr('y', newY).text(d.value).transition(200).style('opacity', 1);
+ z = "polygon."+d3.select(this).attr("class");
+ g.selectAll("polygon").transition(200).style("fill-opacity", 0.1);
+ g.selectAll(z).transition(200).style("fill-opacity", .7);
+ })
+ .on('mouseout', function(){
+ tooltip.transition(200).style('opacity', 0);
+ g.selectAll("polygon").transition(200).style("fill-opacity", cfg.opacityArea);
+ })
+ .append("svg:title")
+ .text(function(j){return Math.max(j.value, 0)});
+
+ series++;
+ });
+ //Tooltip
+ tooltip = g.append('text').style('opacity', 0).style('font-family', 'sans-serif').style('font-size', '13px');
+ }
+}
+
+</script>
+<script type="text/javascript">window.onload = function() {
+ var cpu = 88.6;
+ var mpi = 11.4;
+ var io = 0.0;
+
+ // Draw radar chart, choose its color based on application classification
+ var radar_data = [[ { axis: "CPU", value: cpu },
+ { axis: "MPI", value: mpi },
+ { axis: "I/O", value: io } ]];
+ var radar_options = { w: 200, h: 200, factor: 0.7, fontSize: 16, radius: 0,
+ opacityArea: 0.64, maxValue: 100,
+ color: function() {
+ if ("cpu" == "io") return "#ed8140";
+ if ("cpu" == "cpu") return "#4fd32e";
+ if ("cpu" == "mpi") return "#409ded";
+ else return "#bb58d6";
+ }
+ };
+ RadarChart.draw("#time_radar", radar_data, radar_options);
+
+ // Wrap the exe path on slashes if necessary
+ var exe_path = document.getElementById("exe_path");
+ exe_path.innerHTML = exe_path.innerHTML.replace(/\//g, '/​'); // insert a zero width space after slashes as a word wrap hint
+
+ // Format our byte value numbers to show at most 2 decimal places, but only if required to attain 3 digits of precision
+ // 1234.56 -> "1234"
+ // 12.3456 -> "12.3"
+ // 1.23456 -> "1.23"
+ // 0.00000 -> "0.00"
+ var formatNumber = function(num) {
+ if (num > 100) return new Number(num).toFixed(0); // 1234.56 -> 1234
+ if (num > 10) return new Number(num).toFixed(1); // 12.3456 -> 12.3
+ else return new Number(num).toFixed(2); // 1.23456 -> 1.23 and 0 -> 0.00
+ }
+
+ // Parse a number, but return 0 for invalid numbers, not NaN
+ var toNumber = function(num_str) {
+ var num = Number(num_str)
+ return isNaN(num) ? 0 : num;
+ }
+
+ // Scale byte values to bytes, kB, MB or GB as appropriate
+ var fillBytes = function(base_name, bytes_str, per_second) {
+ var units = "bytes";
+ var scale = 1.0;
+ var bytes = Number(bytes_str)
+ if (isNaN(bytes) ) { units = "" ; }
+ else if (bytes > 1000000000) { units = "GB"; scale = 1*1000*1000*1000; }
+ else if (bytes > 1000000 ) { units = "MB"; scale = 1*1000*1000 }
+ else if (bytes > 1000 ) { units = "kB"; scale = 1*1000 }
+ var num = isNaN(bytes) ? bytes_str : formatNumber(bytes / scale);
+ var elementNum = document.getElementById(base_name + "_num");
+ elementNum.innerHTML = num;
+ var elementUnits = document.getElementById(base_name + "_units");
+ elementUnits.innerHTML = units + (per_second && !isNaN(bytes) ? "/s" : "");
+ };
+
+ // Fill all the byte numbers and their units with the scaled values
+ fillBytes("mpi_colrate", "1.65e+02", true);
+ fillBytes("mpi_p2prate", "0.00e+00", true);
+ fillBytes("io_readrate", "0.00e+00", true);
+ fillBytes("io_writerate", "0.00e+00", true);
+ fillBytes("ram_mean", "2.33e+07", false);
+ fillBytes("ram_peak", "2.35e+07", false);
+
+ // Set widths for all graphs
+ var bar = function(name, width) {
+ var rounded = Math.round(width);
+ if (!isFinite(rounded) || rounded < 1)
+ document.getElementById(name).style.width = "1px"
+ else
+ document.getElementById(name).style.width = rounded + "px";
+ };
+ bar("cpu_bar", cpu * 2);
+ bar("mpi_bar", mpi * 2);
+ bar("io_bar", io * 2);
+
+ bar("cpu_num_bar", toNumber("50.0") / 2);
+ bar("cpu_vec_bar", toNumber("50.0") / 2);
+ bar("cpu_mem_bar", toNumber("0.0") / 2);
+ bar("cpu_other_bar", toNumber("0.0") / 2);
+
+ bar("mpi_col_bar", toNumber("100.0") / 2);
+ bar("mpi_p2p_bar", toNumber("0.0") / 2);
+ var mpi_scale = Math.max(toNumber("1.65e+02"), toNumber("0.00e+00"));
+ bar("mpi_colrate_bar", 50 * toNumber("1.65e+02") / mpi_scale);
+ bar("mpi_p2prate_bar", 50 * toNumber("0.00e+00") / mpi_scale);
+
+ bar("io_read_bar", toNumber("0.0") / 2);
+ bar("io_write_bar", toNumber("0.0") / 2);
+ var io_scale = Math.max(toNumber("0.00e+00"), toNumber("0.00e+00"));
+ bar("io_readrate_bar", 50 * toNumber("0.00e+00") / io_scale);
+ bar("io_writerate_bar", 50 * toNumber("0.00e+00") / io_scale);
+
+ bar("ram_mean_bar", 50 * toNumber("2.33e+07") / toNumber("2.35e+07"));
+ bar("ram_peak_bar", 50);
+ bar("ram_node_bar", toNumber("2.8") / 2);
+
+ // Formatting replacement helper function
+ var replaceIn = function(elementId, re, new_text) {
+ element = document.getElementById(elementId);
+ element.innerHTML = element.innerHTML.replace(re, new_text);
+ };
+
+ // Add formatting for the overview advice section
+ replaceIn('overview_advice', /CPU/g, '<span class="cpu_span">CPU</span>');
+ replaceIn('overview_advice', /MPI/g, '<span class="mpi_span">MPI</span>');
+ replaceIn('overview_advice', /I\/O/g, '<span class="io_span">I\/O</span>');
+
+ // Add formatting for the CPU advice section
+ if ( 88.6 < 0.05 )
+ replaceIn('cpu_explanation', /application code/g, '<span class="cpu_span">application code</span>');
+ replaceIn('cpu_explanation', /vectorized instructions/g, '<span class="cpu_vec_span">vectorized instructions</span>');
+ replaceIn('cpu_explanation', /memory accesses/g, '<span class="cpu_mem_span">memory accesses</span>');
+ replaceIn('cpu_explanation', /memory-bound/g, '<span class="cpu_mem_span">memory-bound</span>');
+ replaceIn('cpu_explanation', /arithmetic-bound/g, '<span class="cpu_num_span">arithmetic-bound</span>');
+ replaceIn('cpu_explanation', /numerical computation/g, '<span class="cpu_num_span">numerical computation</span>');
+
+ // Add formatting for the MPI advice section
+ if ( 11.4 < 0.05 )
+ replaceIn('mpi_explanation', /MPI/g, '<span class="mpi_span">MPI</span>');
+ replaceIn('mpi_explanation', /collective calls/g, '<span class="mpi_col_span">collective calls</span>');
+ replaceIn('mpi_explanation', /point-to-point calls/g, '<span class="mpi_p2p_span">point-to-point calls</span>');
+ replaceIn('mpi_explanation', / very low/g, ' <span class="mpi_colrate_span">very low</span>');
+ replaceIn('mpi_explanation', / low/g, ' <span class="mpi_colrate_span">low</span>');
+ replaceIn('mpi_explanation', / average/g, ' <span class="mpi_colrate_span">average</span>');
+ replaceIn('mpi_explanation', / very high/g, ' <span class="mpi_colrate_span">very high</span>');
+ replaceIn('mpi_explanation', / high/g, ' <span class="mpi_colrate_span">high</span>');
+
+ // Add formatting for the I/O advice section
+ if ( 0.0 < 0.05 )
+ replaceIn('io_explanation', /I\/O/g, '<span class="io_span">I/O</span>');
+ replaceIn('io_explanation', /read operations/g, '<span class="io_read_span">read operations</span>');
+ replaceIn('io_explanation', /write operations/g, '<span class="io_write_span">write operations</span>');
+ replaceIn('io_explanation', / very low/g, ' <span class="io_readrate_span">very low</span>');
+ replaceIn('io_explanation', / low/g, ' <span class="io_readrate_span">low</span>');
+ replaceIn('io_explanation', / average/g, ' <span class="io_readrate_span">average</span>');
+ replaceIn('io_explanation', / very high/g, ' <span class="io_readrate_span">very high</span>');
+ replaceIn('io_explanation', / high/g, ' <span class="io_readrate_span">high</span>');
+
+ // Add formatting for the RAM advice section
+ replaceIn('ram_explanation', /high/g, ' <span class="ram_node_span">high</span>');
+ replaceIn('ram_explanation', /well-balanced/g, '<span class="ram_peak_span">well-balanced</span>');
+ replaceIn('ram_explanation', /significant variation/g, '<span class="ram_peak_span">significant variation</span>');
+ replaceIn('ram_explanation', /peak node memory usage/g, '<span class="ram_node_span">peak node memory usage</span>');
+
+ // Hide the error warning if the script got this far
+ document.getElementById('error').style.display = 'none';
+};
+</script>
+</head>
+
+<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> <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> <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> <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> <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> <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> <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>
diff --git a/docs.it4i/software/debuggers/mympiprog_32p_2014-10-15_16-56.txt b/docs.it4i/software/debuggers/mympiprog_32p_2014-10-15_16-56.txt
new file mode 100644
index 0000000000000000000000000000000000000000..de8449179640fd943a9f007f9eda084b11f2a455
--- /dev/null
+++ b/docs.it4i/software/debuggers/mympiprog_32p_2014-10-15_16-56.txt
@@ -0,0 +1,50 @@
+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.
+
+
diff --git a/docs.it4i/software/debuggers/papi.md b/docs.it4i/software/debuggers/papi.md
new file mode 100644
index 0000000000000000000000000000000000000000..c1c519ecdfc52b5b1675b811d5a4224584a7b948
--- /dev/null
+++ b/docs.it4i/software/debuggers/papi.md
@@ -0,0 +1,237 @@
+# PAPI
+
+## Introduction
+
+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](../../modules-matrix/) papi:
+
+```console
+$ ml papi
+```
+
+This will load the default version. Execute module avail papi for a list of installed versions.
+
+## Utilities
+
+The bin directory of PAPI (which is automatically added to $PATH upon loading the module) contains various utilites.
+
+### Papi_avail
+
+Prints which preset events are available on the current CPU. The third column indicated whether the preset event is available on the current CPU.
+
+```console
+$ 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
+ ....
+```
+
+### Papi_native_avail
+
+Prints which native events are available on the current CPU.
+
+### 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:
+
+* **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 $INCLUDE.
+
+### High Level API
+
+Please refer to [this description of the High level API](http://icl.cs.utk.edu/projects/papi/wiki/PAPIC:High_Level).
+
+### Low Level API
+
+Please refer to [this description of the Low level API](http://icl.cs.utk.edu/projects/papi/wiki/PAPIC:Low_Level).
+
+### Timers
+
+PAPI provides the most accurate timers the platform can support. [See](http://icl.cs.utk.edu/projects/papi/wiki/PAPIC:Timers).
+
+### System Information
+
+PAPI can be used to query some system infromation, such as CPU name and MHz. [See](http://icl.cs.utk.edu/projects/papi/wiki/PAPIC:System_Information).
+
+## Example
+
+The following example prints MFLOPS rate of a naive matrix-matrix multiplication:
+
+```cpp
+ #include <stdlib.h>
+ #include <stdio.h>
+ #include "papi.h"
+ #define SIZE 1000
+
+ int main(int argc, char **argv) {
+ float matrixa[SIZE][SIZE], matrixb[SIZE][SIZE], mresult[SIZE][SIZE];
+ float real_time, proc_time, mflops;
+ long long flpins;
+ int retval;
+ int i,j,k;
+
+ /* Initialize the Matrix arrays */
+ for ( i=0; i<SIZE*SIZE; i++ ){
+ mresult[0][i] = 0.0;
+ matrixa[0][i] = matrixb[0][i] = rand()*(float)1.1;
+ }
+
+ /* Setup PAPI library and begin collecting data from the counters */
+ if((retval=PAPI_flops( &real_time, &proc_time, &flpins, &mflops))<PAPI_OK)
+ printf("Error!");
+
+ /* A naive Matrix-Matrix multiplication */
+ for (i=0;i<SIZE;i++)
+ for(j=0;j<SIZE;j++)
+ for(k=0;k<SIZE;k++)
+ mresult[i][j]=mresult[i][j] + matrixa[i][k]*matrixb[k][j];
+
+ /* Collect the data into the variables passed in */
+ if((retval=PAPI_flops( &real_time, &proc_time, &flpins, &mflops))<PAPI_OK)
+ printf("Error!");
+
+ printf("Real_time:t%fnProc_time:t%fnTotal flpins:t%lldnMFLOPS:tt%fn", real_time, proc_time, flpins, mflops);
+ PAPI_shutdown();
+ return 0;
+ }
+```
+
+Now compile and run the example :
+
+```console
+$ gcc matrix.c -o matrix -lpapi
+$ ./matrix
+ Real_time: 8.852785
+ Proc_time: 8.850000
+ Total flpins: 6012390908
+ MFLOPS: 679.366211
+```
+
+Let's try with optimizations enabled :
+
+```console
+$ 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 :
+
+```cpp
+ 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:
+
+```console
+$ gcc -O3 matrix.c -o matrix -lpapi
+$ ./matrix
+ Real_time: 8.795956
+ Proc_time: 8.790000
+ Total flpins: 18700983160
+ MFLOPS: 2127.529297
+```
+
+### Intel Xeon Phi
+
+!!! note
+ 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](../../anselm/software/intel-xeon-phi/) native applications, you need to load module with " -mic" suffix, for example " papi/5.3.2-mic" :
+
+```console
+$ ml papi/5.3.2-mic
+```
+
+Then, compile your application in the following way:
+
+```console
+$ ml 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 set LD_LIBRARY_PATH:
+
+```console
+$ qsub -q qmic -A NONE-0-0 -I
+$ ssh mic0
+$ export LD_LIBRARY_PATH="/apps/tools/papi/5.4.0-mic/lib/"
+$ ./matrix-mic
+```
+
+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 :
+
+```console
+$ /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:
+
+```console
+$ ml papi/5.4.0
+$ icc matrix-offload.c -o matrix-offload -offload-option,mic,compiler,"-L$PAPI_HOME-mic/lib -lpapi" -lpapi
+```
+
+## References
+
+1. [Main project page](http://icl.cs.utk.edu/papi/)
+1. [Wiki](http://icl.cs.utk.edu/projects/papi/wiki/Main_Page)
+1. [API Documentation](http://icl.cs.utk.edu/papi/docs/)
diff --git a/docs.it4i/software/debuggers/scalasca.md b/docs.it4i/software/debuggers/scalasca.md
new file mode 100644
index 0000000000000000000000000000000000000000..653c0f78de238809d9ac2f038d5de361065157fa
--- /dev/null
+++ b/docs.it4i/software/debuggers/scalasca.md
@@ -0,0 +1,70 @@
+# 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](../../modules-matrix/) installed on Anselm:
+
+* scalasca2/2.0-gcc-openmpi, for usage with [GNU Compiler](../compilers/) and [OpenMPI](../mpi/Running_OpenMPI/),
+* scalasca2/2.0-icc-impi, for usage with [Intel Compiler](../compilers/) and [Intel MPI](../mpi/running-mpich2/).
+
+## 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.
+1. Runtime measurement, running the application with the Scalasca profiler to collect performance data.
+1. Analysis of reports
+
+### Instrumentation
+
+Instrumentation via " scalasca -instrument" is discouraged. Use [Score-P instrumentation](score-p/).
+
+### 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 :
+
+```console
+ $ scalasca -analyze mpirun -np 4 ./mympiprogram
+```
+
+Some notable Scalasca 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.**
+
+!!! note
+ Scalasca can generate a huge amount of data, especially if tracing is enabled. Please consider saving the data to a [scratch directory](../../storage/storage/).
+
+### Analysis of Reports
+
+For the analysis, you must have [Score-P](score-p/) and [CUBE](cube/) 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 :
+
+```console
+scalasca -examine [options] <experiment_directory>
+```
+
+If you do not wish to launch the GUI tool, use the "-s" option :
+
+```console
+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/) on usage of the GUI viewer.
+
+## References
+
+1. <http://www.scalasca.org/>
diff --git a/docs.it4i/software/debuggers/score-p.md b/docs.it4i/software/debuggers/score-p.md
new file mode 100644
index 0000000000000000000000000000000000000000..a30df38e784ab00035208d9d824264081d3ed38c
--- /dev/null
+++ b/docs.it4i/software/debuggers/score-p.md
@@ -0,0 +1,117 @@
+# 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.
+
+Score-P can be used as an instrumentation tool for [Scalasca](scalasca/).
+
+## Installed Versions
+
+There are currently two versions of Score-P version 1.2.6 [modules](../../modules-matrix/) installed on Anselm :
+
+* scorep/1.2.3-gcc-openmpi, for usage with [GNU Compiler](../../anselm/software/compilers/) and [OpenMPI](../mpi/Running_OpenMPI/)
+* scorep/1.2.3-icc-impi, for usage with [Intel Compiler](../../anselm/software/compilers/)> and [Intel MPI](../mpi/running-mpich2/)>.
+
+## Instrumentation
+
+There are three ways to instrument your parallel applications in order to enable performance data collection:
+
+1. Automated instrumentation using compiler
+1. Manual instrumentation using API calls
+1. 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:
+
+```console
+$ mpif90 -c foo.f90
+$ mpif90 -c bar.f90
+$ mpif90 -o myapp foo.o bar.o
+```
+
+with:
+
+```console
+$ 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, 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++ :
+
+```cpp
+#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 )
+}
+```
+
+ and Fortran :
+
+```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++ :
+
+```cpp
+void foo(...)
+{
+ /* declarations */
+ #pragma pomp inst begin(foo)
+ ...
+ if (<condition>)
+ {
+ #pragma pomp inst altend(foo)
+ return;
+ }
+ ...
+ #pragma pomp inst end(foo)
+}
+```
+
+and in Fortran :
+
+```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.
diff --git a/docs.it4i/software/debuggers/total-view.md b/docs.it4i/software/debuggers/total-view.md
new file mode 100644
index 0000000000000000000000000000000000000000..aebe91a523f00cc82fe566fed7f2102af9762509
--- /dev/null
+++ b/docs.it4i/software/debuggers/total-view.md
@@ -0,0 +1,167 @@
+# 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:
+
+```console
+ 1 user can debug up 64 processes, or
+ 32 users can debug 2 processes, etc.
+```
+
+Debugging of GPU accelerated codes is also supported.
+
+You can check the status of the licenses [here (Salomon)](https://extranet.it4i.cz/rsweb/anselm/license/Totalview) or type (Anselm):
+
+```console
+$ cat /apps/user/licenses/totalview_features_state.txt
+
+ # totalview
+ # -------------------------------------------------
+ # FEATURE TOTAL USED AVAIL
+ # -------------------------------------------------
+ TotalView_Team 64 0 64
+ Replay 64 0 64
+ CUDA 64 0 64
+```
+
+## Compiling Code to Run With TotalView
+
+### Modules
+
+Load all necessary modules to compile the code. For example:
+
+```console
+ ml intel
+```
+
+Load the TotalView module:
+
+```console
+ ml TotalView
+ ml totalview
+```
+
+Compile the code:
+
+```console
+ 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:
+
+!!! note
+ **-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:
+
+```console
+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.
+
+From the login node an interactive session with X windows forwarding (-X option) can be started by following command (for Anselm use 16 threads):
+
+```console
+$ 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:
+
+```console
+totalview test_debug
+```
+
+### Debugging a Parallel Code - Option 1
+
+To debug a parallel code compiled with **OpenMPI** you need to setup your TotalView environment:
+
+!!! hint
+ 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.
+
+```console
+proc mpi_auto_run_starter {loaded_id} {
+ set starter_programs {mpirun mpiexec orterun}
+ set executable_name [TV::symbol get $loaded_id full_pathname]
+ set file_component [file tail $executable_name]
+
+ if {[lsearch -exact $starter_programs $file_component] != -1} {
+ puts "*************************************"
+ puts "Automatically starting $file_component"
+ puts "*************************************"
+ dgo
+ }
+}
+
+# Append this function to TotalView's image load callbacks so that
+# TotalView run this program automatically.
+
+dlappend TV::image_load_callbacks mpi_auto_run_starter
+```
+
+The source code of this function can be also found in
+
+```console
+$ /apps/all/OpenMPI/1.10.1-GNU-4.9.3-2.25/etc/openmpi-totalview.tcl #Salomon
+
+$ /apps/mpi/openmpi/intel/1.6.5/etc/openmpi-totalview.tcl #Anselm
+```
+
+You can also add only following line to you ~/.tvdrc file instead of
+the entire function:
+
+```console
+$ source /apps/all/OpenMPI/1.10.1-GNU-4.9.3-2.25/etc/openmpi-totalview.tcl #Salomon
+
+$ source /apps/mpi/openmpi/intel/1.6.5/etc/openmpi-totalview.tcl #Anselm
+```
+
+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:
+
+```console
+$ 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.
+
+The following example shows how to start debugging session with Intel MPI:
+
+```console
+$ ml intel
+$ ml 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.
+
+## 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.
diff --git a/docs.it4i/software/debuggers/valgrind.md b/docs.it4i/software/debuggers/valgrind.md
new file mode 100644
index 0000000000000000000000000000000000000000..b91eeb7a0642fb603a43029f577fb3310acee827
--- /dev/null
+++ b/docs.it4i/software/debuggers/valgrind.md
@@ -0,0 +1,273 @@
+# 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](../../modules-matrix/) valgrind/3.9.0-impi. After loading the module, this version replaces the default valgrind.
+
+There are two versions of Valgrind available on the Salomon.
+
+* 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 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).
+
+For example, lets look at this C code, which has two problems :
+
+```cpp
+#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 :
+
+```console
+$ module add intel
+$ icc -g valgrind-example.c -o valgrind-example
+```
+
+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.
+
+```console
+$ 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 :
+
+```console
+$ 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 :
+
+```console
+$ 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 :
+
+```console
+ ==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 library:
+
+* Anselm: /apps/tools/valgrind/3.9.0/impi/lib/valgrind/libmpiwrap-amd64-linux.so
+* Salomon: $EBROOTVALGRIND/lib/valgrind/libmpiwrap-amd64-linux.so
+
+which must be included in the LD_PRELOAD environment variable.
+
+Lets look at this MPI example :
+
+```cpp
+#include <stdlib.h>
+#include <mpi.h>
+
+int main(int argc, char *argv[])
+{
+ int *data = malloc(sizeof(int)*99);
+
+ MPI_Init(&argc, &argv);
+ MPI_Bcast(data, 100, MPI_INT, 0, MPI_COMM_WORLD);
+ MPI_Finalize();
+
+ return 0;
+}
+```
+
+There are two errors - use of uninitialized memory and invalid length of the buffer. Lets debug it with valgrind :
+
+```console
+$ 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)
+
+```console
+ ==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 broadcast) and use of unaddresable memory on both processes.
diff --git a/docs.it4i/software/debuggers/vampir.md b/docs.it4i/software/debuggers/vampir.md
new file mode 100644
index 0000000000000000000000000000000000000000..93d11f0ca571bd8dadd401b7f6173a3403683476
--- /dev/null
+++ b/docs.it4i/software/debuggers/vampir.md
@@ -0,0 +1,24 @@
+# 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/)) first to collect the traces.
+
+
+
+## Installed Versions
+
+```console
+$ ml av Vampir
+```
+
+```console
+$ ml Vampir
+$ vampir &
+```
+
+## User Manual
+
+You can find the detailed user manual in PDF format in $EBROOTVAMPIR/doc/vampir-manual.pdf
+
+## References
+
+1. <https://www.vampir.eu>
diff --git a/docs.it4i/software/machine-learning/tensorflow.md b/docs.it4i/software/machine-learning/tensorflow.md
index aa433e3e7952731abd500735fbfa8386d8734af5..26344cf2d7a33b7de70fe11c992cf410d6e296e9 100644
--- a/docs.it4i/software/machine-learning/tensorflow.md
+++ b/docs.it4i/software/machine-learning/tensorflow.md
@@ -42,7 +42,6 @@ Salomon provides beside others these three different TensorFlow modules:
* Python/3.6.1
* protobuf/3.2.0-GCC-7.1.0-2.28-Python-3.6.1
-
## TensorFlow application example
After loading one of the available TensorFlow modules, you can check the functionality running the following python script.
diff --git a/docs.it4i/software/mpi/Running_OpenMPI.md b/docs.it4i/software/mpi/Running_OpenMPI.md
new file mode 100644
index 0000000000000000000000000000000000000000..5f2606bc2671012b95e53831eb63aa0c95451500
--- /dev/null
+++ b/docs.it4i/software/mpi/Running_OpenMPI.md
@@ -0,0 +1,207 @@
+# Running OpenMPI
+
+## OpenMPI Program Execution
+
+The OpenMPI programs may be executed only via the PBS Workload manager, by entering an appropriate queue. On Anselm, the **bullxmpi-1.2.4.1** and **OpenMPI 1.6.5** are OpenMPI based MPI implementations. On Salomon, the **OpenMPI 1.8.6** is OpenMPI based MPI implementation.
+
+### Basic Usage
+
+!!! note
+ Use the mpiexec to run the OpenMPI code.
+
+Example (for Anselm):
+
+```console
+$ 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
+$ ml 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
+```
+
+!!! note
+ 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
+
+```console
+$ 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.
+
+!!! note
+ 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 (**on Salomon try 24 threads in following examples**).
+
+```console
+$ qsub -q qexp -l select=4:ncpus=16:mpiprocs=1:ompthreads=16 -I
+$ ml 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.
+
+```console
+$ qsub -q qexp -l select=4:ncpus=16:mpiprocs=2:ompthreads=8 -I
+$ ml 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.
+
+```console
+$ qsub -q qexp -l select=4:ncpus=16:mpiprocs=16:ompthreads=1 -I
+$ ml 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
+
+!!! note
+ 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:
+
+```console
+$ export GOMP_CPU_AFFINITY="0-15"
+```
+
+or this one for Intel OpenMP:
+
+```console
+$ 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:
+
+```console
+$ export OMP_PROC_BIND=true
+$ export OMP_PLACES=cores
+```
+
+## 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
+
+```console
+ cn110.bullx
+ cn109.bullx
+ cn108.bullx
+ cn17.bullx
+```
+
+Use the hostfile to control process placement
+
+```console
+$ 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
+
+!!! note
+ Appropriate binding may boost performance of your application.
+
+Example rankfile
+
+```console
+ 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
+
+```console
+$ 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:
+
+```console
+$ mpiexec -bysocket -bind-to-socket --report-bindings echo
+$ mpiexec -bysocket -bind-to-socket numactl --show
+$ mpiexec -bysocket -bind-to-socket echo $OMP_NUM_THREADS
+```
+
+## Changes in OpenMPI 1.8
+
+Some options have changed in OpenMPI version 1.8.
+
+| version 1.6.5 | version 1.8.1 |
+| ---------------- | ------------------- |
+| --bind-to-none | --bind-to none |
+| --bind-to-core | --bind-to core |
+| --bind-to-socket | --bind-to socket |
+| -bysocket | --map-by socket |
+| -bycore | --map-by core |
+| -pernode | --map-by ppr:1:node |
diff --git a/docs.it4i/software/mpi/mpi.md b/docs.it4i/software/mpi/mpi.md
new file mode 100644
index 0000000000000000000000000000000000000000..8f3b580fb1169197bceaa4d8a3e614b79b764a3e
--- /dev/null
+++ b/docs.it4i/software/mpi/mpi.md
@@ -0,0 +1,141 @@
+# MPI
+
+## Setting Up MPI Environment
+
+The Salomon cluster provides several implementations of the MPI library:
+
+| MPI Library | Thread support |
+| ----------------- | ------------------------------------------------------------------------------------------------------ |
+| **Intel MPI 4.1** | Full thread support up to, MPI_THREAD_MULTIPLE |
+| **Intel MPI 5.0** | Full thread support up to, MPI_THREAD_MULTIPLE |
+| OpenMPI 1.8.6 | Full thread support up to, MPI_THREAD_MULTIPLE, MPI-3.0, support |
+| SGI MPT 2.12 | |
+
+MPI libraries are activated via the environment modules.
+
+Look up section modulefiles/mpi in module avail
+
+```console
+$ ml av
+ ------------------------------ /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:
+
+```console
+$ ml 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/) .
+
+To use OpenMPI with the intel compiler suite, use
+
+```console
+$ ml 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
+
+```console
+$ mpicc -v
+$ mpif77 -v
+$ mpif90 -v
+```
+
+When using Intel MPI, use the following MPI wrappers:
+
+```console
+$ mpicc
+$ mpiifort
+```
+
+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:
+
+```cpp
+// 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
+
+```console
+$ 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.
+
+!!! note
+ 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.
+
+**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.
+
+!!! note
+ 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/) based MPI.
+
+The Intel MPI may run on the[Intel Xeon Ph](../intel-xeon-phi/)i accelerators as well. Read more on [how to run Intel MPI on accelerators](../intel-xeon-phi/).
diff --git a/docs.it4i/software/mpi/mpi4py-mpi-for-python.md b/docs.it4i/software/mpi/mpi4py-mpi-for-python.md
new file mode 100644
index 0000000000000000000000000000000000000000..a8c964d31d2f653f41c14ca245d6453bd7d3efbe
--- /dev/null
+++ b/docs.it4i/software/mpi/mpi4py-mpi-for-python.md
@@ -0,0 +1,171 @@
+# MPI4Py (MPI for 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).
+
+MPI4Py is available in standard Python modules on the clusters.
+
+## Modules
+
+MPI4Py is build for OpenMPI. Before you start with MPI4Py you need to load Python and OpenMPI modules.
+
+```console
+$ ml av Python/
+--------------------------------------- /apps/modules/lang -------------------------
+ Python/2.7.8-intel-2015b Python/2.7.11-intel-2016a Python/3.5.1-intel-2017.00
+ Python/2.7.11-intel-2017a Python/2.7.9-foss-2015b Python/2.7.9-intel-2015b
+ Python/2.7.11-foss-2016a Python/3.5.2-foss-2016a Python/3.5.1
+ Python/2.7.9-foss-2015g Python/3.4.3-intel-2015b Python/2.7.9
+ Python/2.7.11-intel-2015b Python/3.5.2
+
+$ ml av OpenMPI/
+--------------------------------------- /apps/modules/mpi --------------------------
+OpenMPI/1.8.6-GCC-4.4.7-system OpenMPI/1.8.8-GNU-4.9.3-2.25 OpenMPI/1.10.1-GCC-4.9.3-2.25
+OpenMPI/1.8.6-GNU-5.1.0-2.25 OpenMPI/1.8.8-GNU-5.1.0-2.25 OpenMPI/1.10.1-GNU-4.9.3-2.25
+ OpenMPI/1.8.8-iccifort-2015.3.187-GNU-4.9.3-2.25 OpenMPI/2.0.2-GCC-6.3.0-2.27
+```
+
+!!! Warning "Flavours"
+
+ * modules Python/x.x.x-intel... - intel MPI
+ * modules Python/x.x.x-foss... - OpenMPI
+ * modules Python/x.x.x - without MPI
+
+## Execution
+
+You need to import MPI to your python program. Include the following line to the python script:
+
+```python
+from mpi4py import MPI
+```
+
+The MPI4Py enabled python programs [execute as any other OpenMPI](Running_OpenMPI/) code.The simpliest way is to run
+
+```console
+$ mpiexec python <script>.py
+```
+
+For example
+
+```console
+$ mpiexec python hello_world.py
+```
+
+## Examples
+
+### Hello World!
+
+```python
+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() # wait for everybody to synchronize
+```
+
+### Collective Communication With NumPy Arrays
+
+```python
+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:
+ A = np.arange(N, dtype=np.float64) # rank 0 has proper data
+else:
+ A = np.empty(N, dtype=np.float64) # 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:
+
+```console
+$ qsub -q qexp -l select=4:ncpus=16:mpiprocs=16:ompthreads=1 -I # Salomon: ncpus=24:mpiprocs=24
+$ ml Python
+$ ml 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://pypi.python.org/pypi/mpi4py).
+
+### Adding numbers
+
+Task: count sum of numbers from 1 to 1 000 000. (There is an easy formula to count the sum of arithmetic sequence, but we are showing the MPI solution with adding numbers one by one).
+
+```python
+#!/usr/bin/python
+
+import numpy
+from mpi4py import MPI
+import time
+
+comm = MPI.COMM_WORLD
+rank = comm.Get_rank()
+size = comm.Get_size()
+
+a = 1
+b = 1000000
+
+perrank = b//size
+summ = numpy.zeros(1)
+
+comm.Barrier()
+start_time = time.time()
+
+temp = 0
+for i in range(a + rank*perrank, a + (rank+1)*perrank):
+ temp = temp + i
+
+summ[0] = temp
+
+if rank == 0:
+ total = numpy.zeros(1)
+else:
+ total = None
+
+comm.Barrier()
+#collect the partial results and add to the total sum
+comm.Reduce(summ, total, op=MPI.SUM, root=0)
+
+stop_time = time.time()
+
+if rank == 0:
+ #add the rest numbers to 1 000 000
+ for i in range(a + (size)*perrank, b+1):
+ total[0] = total[0] + i
+ print ("The sum of numbers from 1 to 1 000 000: ", int(total[0]))
+ print ("time spent with ", size, " threads in milliseconds")
+ print ("-----", int((time.time()-start_time)*1000), "-----")
+```
+
+Execute the code above as:
+
+```console
+$ qsub -I -q qexp -l select=4:ncpus=16,walltime=00:30:00
+
+$ ml Python/3.5.2-intel-2017.00
+
+$ mpirun -n 2 python myprogram.py
+```
+
+You can increase n and watch time lowering.
diff --git a/docs.it4i/software/mpi/running-mpich2.md b/docs.it4i/software/mpi/running-mpich2.md
new file mode 100644
index 0000000000000000000000000000000000000000..7b37a811802ffe6aa142cad5773cfc20e842b6fd
--- /dev/null
+++ b/docs.it4i/software/mpi/running-mpich2.md
@@ -0,0 +1,155 @@
+# 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
+
+!!! note
+ Use the mpirun to execute the MPICH2 code.
+
+Example:
+
+```console
+$ qsub -q qexp -l select=4:ncpus=16 -I
+ qsub: waiting for job 15210.srv11 to start
+ qsub: job 15210.srv11 ready
+$ ml impi
+$ mpirun -ppn 1 -hostfile $PBS_NODEFILE ./helloworld_mpi.x
+ Hello world! from rank 0 of 4 on host cn17
+ Hello world! from rank 1 of 4 on host cn108
+ Hello world! from rank 2 of 4 on host cn109
+ Hello world! from rank 3 of 4 on host cn110
+```
+
+In this example, we allocate 4 nodes via the express queue interactively. We set up the intel MPI environment and interactively run the helloworld_mpi.x program. We request MPI to spawn 1 process per node.
+Note that the executable helloworld_mpi.x must be available within the same path on all nodes. This is automatically fulfilled on the /home and /scratch filesystem.
+
+You need to preload the executable, if running on the local scratch /lscratch filesystem
+
+```console
+$ pwd
+ /lscratch/15210.srv11
+$ mpirun -ppn 1 -hostfile $PBS_NODEFILE cp /home/username/helloworld_mpi.x .
+$ mpirun -ppn 1 -hostfile $PBS_NODEFILE ./helloworld_mpi.x
+ Hello world! from rank 0 of 4 on host cn17
+ Hello world! from rank 1 of 4 on host cn108
+ Hello world! from rank 2 of 4 on host cn109
+ Hello world! from rank 3 of 4 on host cn110
+```
+
+In this example, we assume the executable helloworld_mpi.x is present on shared home directory. We run the cp command via mpirun, copying the executable from shared home to local scratch . Second mpirun will execute the binary in the /lscratch/15210.srv11 directory on nodes cn17, cn108, cn109 and cn110, one process per node.
+
+!!! note
+ MPI process mapping may be controlled by PBS parameters.
+
+The mpiprocs and ompthreads parameters allow for selection of number of running MPI processes per node as well as number of OpenMP threads per MPI process.
+
+### One MPI Process Per Node
+
+Follow this example to run one MPI process per node, 16 threads per process. Note that no options to mpirun are needed
+
+```console
+$ qsub -q qexp -l select=4:ncpus=16:mpiprocs=1:ompthreads=16 -I
+$ ml 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.
+
+```console
+$ qsub -q qexp -l select=4:ncpus=16:mpiprocs=2:ompthreads=8 -I
+$ ml 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.
+
+```console
+$ qsub -q qexp -l select=4:ncpus=16:mpiprocs=16:ompthreads=1 -I
+$ ml 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
+
+!!! note
+ 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:
+
+```console
+$ export GOMP_CPU_AFFINITY="0-15"
+```
+
+or this one for Intel OpenMP:
+
+```console
+$ 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:
+
+```console
+$ export OMP_PROC_BIND=true
+$ export OMP_PLACES=cores
+```
+
+## 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
+
+```console
+ cn110.bullx
+ cn109.bullx
+ cn108.bullx
+ cn17.bullx
+ cn108.bullx
+```
+
+Use the machinefile to control process placement
+
+```console
+$ 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
+
+```console
+$ 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/) provides details on how to run Intel MPI code on Xeon Phi architecture.
diff --git a/mkdocs.yml b/mkdocs.yml
index 38791429b7d4a485188c2a890524d0ec2611ced9..5be70b08f8a636cbca99e00ac651d6417fdea278 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -58,6 +58,8 @@ pages:
- Remote Visualization: anselm/remote-visualization.md
- PRACE User Support: anselm/prace.md
- 'Software':
+ - Lmod Environment: software/lmod.md
+ - Modules Matrix: modules-matrix.md
- 'ANSYS':
- Introduction: software/ansys/ansys.md
- ANSYS CFX: software/ansys/ansys-cfx.md
@@ -77,14 +79,30 @@ pages:
- Orca: software/chemistry/orca.md
- NWChem: software/chemistry/nwchem.md
- Phono3py: software/chemistry/phono3py.md
- - EasyBuild: software/easybuild.md
+ - 'Debuggers':
+ - Introduction: software/debuggers/Introduction.md
+ - Aislinn: software/debuggers/aislinn.md
+ - Allinea Forge (DDT,MAP): software/debuggers/allinea-ddt.md
+ - Allinea Performance Reports: software/debuggers/allinea-performance-reports.md
+ - CUBE: software/debuggers/cube.md
+ - Intel Performance Counter Monitor: software/debuggers/intel-performance-counter-monitor.md
+ - Intel VTune Amplifier XE: software/debuggers/intel-vtune-amplifier.md
+ - PAPI: software/debuggers/papi.md
+ - Scalasca: software/debuggers/scalasca.md
+ - Score-P: software/debuggers/score-p.md
+ - Total View: software/debuggers/total-view.md
+ - Valgrind: software/debuggers/valgrind.md
+ - Vampir: software/debuggers/vampir.md
- ISV Licenses: software/isv_licenses.md
- Java: software/java.md
- - Lmod Environment: software/lmod.md
- - Modules Matrix: modules-matrix.md
- 'Machine larning':
- Introduction: software/machine-learning/introduction.md
- TensorFlow: software/machine-learning/tensorflow.md
+ - 'MPI':
+ - Introduction: software/mpi/mpi.md
+ - MPI4Py (MPI for Python): software/mpi/mpi4py-mpi-for-python.md
+ - Running Open MPI: software/mpi/Running_OpenMPI.md
+ - Running MPICH2: software/mpi/running-mpich2.md
- 'Numerical languages':
- Introduction: software/numerical-languages/introduction.md
- R: software/numerical-languages/r.md
@@ -94,21 +112,14 @@ pages:
- OpenCoarrays: software/numerical-languages/opencoarrays.md
- OpenFOAM: software/openfoam.md
- Operating System: software/operating-system.md
- - Singularity Container: software/singularity.md
- - Spack: software/spack.md
+ - 'Tools':
+ - EasyBuild: software/easybuild.md
+ - Singularity Container: software/singularity.md
+ - Spack: software/spack.md
- Salomon Software:
- Available Modules: modules-salomon.md
- Available Modules on UV: modules-salomon-uv.md
- Compilers: salomon/software/compilers.md
- - 'Debuggers':
- - Introduction: salomon/software/debuggers/Introduction.md
- - Aislinn: salomon/software/debuggers/aislinn.md
- - Allinea Forge (DDT,MAP): salomon/software/debuggers/allinea-ddt.md
- - Allinea Performance Reports: salomon/software/debuggers/allinea-performance-reports.md
- - Intel VTune Amplifier XE: salomon/software/debuggers/intel-vtune-amplifier.md
- - Total View: salomon/software/debuggers/total-view.md
- - Valgrind: salomon/software/debuggers/valgrind.md
- - Vampir: salomon/software/debuggers/vampir.md
- 'Intel Suite':
- Introduction: salomon/software/intel-suite/intel-parallel-studio-introduction.md
- Intel Advisor: salomon/software/intel-suite/intel-advisor.md
@@ -120,26 +131,9 @@ pages:
- Intel TBB: salomon/software/intel-suite/intel-tbb.md
- Intel Trace Analyzer and Collector: salomon/software/intel-suite/intel-trace-analyzer-and-collector.md
- Intel Xeon Phi: salomon/software/intel-xeon-phi.md
- - 'MPI':
- - Introduction: salomon/software/mpi/mpi.md
- - MPI4Py (MPI for Python): salomon/software/mpi/mpi4py-mpi-for-python.md
- - Running Open MPI: salomon/software/mpi/Running_OpenMPI.md
- ParaView: salomon/software/paraview.md
- Anselm Software:
- Available Modules: modules-anselm.md
- - 'Debuggers':
- - Allinea Forge (DDT,MAP): anselm/software/debuggers/allinea-ddt.md
- - Allinea Performance Reports: anselm/software/debuggers/allinea-performance-reports.md
- - CUBE: anselm/software/debuggers/cube.md
- - Intel Performance Counter Monitor: anselm/software/debuggers/intel-performance-counter-monitor.md
- - Intel VTune Amplifier: anselm/software/debuggers/intel-vtune-amplifier.md
- - PAPI: anselm/software/debuggers/papi.md
- - Scalasca: anselm/software/debuggers/scalasca.md
- - Score-P: anselm/software/debuggers/score-p.md
- - Total View: anselm/software/debuggers/total-view.md
- - VNC: anselm/software/debuggers/debuggers.md
- - Valgrind: anselm/software/debuggers/valgrind.md
- - Vampir: anselm/software/debuggers/vampir.md
- Compilers: anselm/software/compilers.md
- GPI-2: anselm/software/gpi2.md
- 'Intel Suite':
@@ -150,11 +144,6 @@ pages:
- Intel MKL: anselm/software/intel-suite/intel-mkl.md
- Intel TBB: anselm/software/intel-suite/intel-tbb.md
- Intel Xeon Phi: anselm/software/intel-xeon-phi.md
- - 'MPI':
- - Introduction: anselm/software/mpi/mpi.md
- - MPI4Py (MPI for Python): anselm/software/mpi/mpi4py-mpi-for-python.md
- - Running Open MPI: anselm/software/mpi/Running_OpenMPI.md
- - Running MPICH2: anselm/software/mpi/running-mpich2.md
- 'Numerical Libraries':
- FFTW: anselm/software/numerical-libraries/fftw.md
- GSL: anselm/software/numerical-libraries/gsl.md
diff --git a/pathcheck.sh b/pathcheck.sh
index 932b80f6c74b02f472857e4e29c2537458e74fff..932b0fb9c118eaa05616f85b7b095786fb9939bd 100644
--- a/pathcheck.sh
+++ b/pathcheck.sh
@@ -1,21 +1,18 @@
#!/bin/bash
-
-
+#the script controls links, only inside the whole directory, doesnt control outside pages
for file in $@; do
-check=$(cat $file | grep -Po "\[.*?\]\([^ ]*?\)" | grep -v "#" | grep -vE "http|@|www|ftp|none" | sed 's/\[.*\]//g' | sed 's/[()]//g' | sed 's/\/$/.md/g')
+check=$(cat "$file" | grep -Po "\[.*?\]\([^ ]*?\)" | grep -v "#" | grep -vE "http|@|www|ftp|none" | sed 's/\[.*\]//g' | sed 's/[()]//g' | sed 's/\/$/.md/g')
if [ ! -z "$check" ]; then
-# echo "\n+++++ $file +++++\n"
wrong=0
for line in $check; do
-#echo $line
-pathtocheck=$(dirname $file)/$line
+pathtocheck=$(dirname "$file")/$line
-if [ -f $(dirname $file)/$line ]; then
+if [ -f $(dirname "$file")/"$line" ]; then
:
#echo "ok $pathtocheck"
else
@@ -24,7 +21,6 @@ else
echo "\n+++++ $file +++++\n"
fi
wrong=1
-# echo "wrong link in $(readlink -m $pathtocheck)"
echo "wrong link in $pathtocheck"
fi
diff --git a/tmp b/tmp
deleted file mode 100644
index 8e54c8dd714e33c57ad06d0d58f4675e278dcc63..0000000000000000000000000000000000000000
--- a/tmp
+++ /dev/null
@@ -1,24 +0,0 @@
- - Bioinformatics: software/bioinformatics.md
- - 'COMSOL':
- - COMSOL: software/comsol/comsol-multiphysics.md
- - EasyBuild: software/easybuild.md
- - 'Chemistry':
- - Introduction: software/numerical-languages/introduction.md
- - Java: software/java.md
- - Licensing and Available Versions: software/comsol/licensing-and-available-versions.md
- - Lmod Environment: software/lmod.md
- - Matlab: software/numerical-languages/matlab.md
- - Matlab 2013-2014: software/numerical-languages/matlab_1314.md
- - Modules Matrix: modules-matrix.md
- - Molpro: software/chemistry/molpro.md
- - 'Numerical languages':
- - NWChem: software/chemistry/nwchem.md
- - Octave: software/numerical-languages/octave.md
- - OpenCoarrays: software/numerical-languages/opencoarrays.md
- - OpenFOAM: software/openfoam.md
- - Operating System: software/operating-system.md
- - Orca: software/chemistry/orca.md
- - Phono3py: software/chemistry/phono3py.md
- - R: software/numerical-languages/r.md
- - Singularity Container: software/singularity.md
- - Spack: software/spack.md