docs.it4i/software/chemistry/gofree-cond1.sh

+#!/bin/bash
+#PBS -A OPEN-6-23
+#PBS -N Si-test1
+#PBS -q qfree 
+#PBS -l select=1:ncpus=24:mpiprocs=24:ompthreads=1
+#PBS -l walltime=01:59:59
+##PBS-l mem=6gb
+#PBS -j oe
+#PBS -S /bin/bash
+module purge
+module load phono3py/0.9.14-ictce-7.3.5-Python-2.7.9
+export OMP_NUM_THREADS=1
+export I_MPI_COMPATIBILITY=4
+##export OMP_STACKSIZE=10gb
+##0	1	2	3	4	10	11	12	13	20	21	22	30	31	40	91	92	93	94	101	102	103	111	112	121	182	183	184	192	193	202	273	274	283	364
+cd $PBS_O_WORKDIR
+phono3py --fc3 --fc2 --dim="2 2 2" --mesh="9 9 9" -c POSCAR  --sigma 0.1 --br --write_gamma --gp="0 1 3 4 10" 
+#phono3py --fc3 --fc2 --dim="2 2 2" --mesh="9 9 9" -c POSCAR  --sigma 0.1 --br --write_gamma --gp="11 12 13 20 21" 
+#phono3py --fc3 --fc2 --dim="2 2 2" --mesh="9 9 9" -c POSCAR  --sigma 0.1 --br --write_gamma --gp="21 22 30 31 40"
+#phono3py --fc3 --fc2 --dim="2 2 2" --mesh="9 9 9" -c POSCAR  --sigma 0.1 --br --write_gamma --gp="91 92 93 94 101"
+#phono3py --fc3 --fc2 --dim="2 2 2" --mesh="9 9 9" -c POSCAR  --sigma 0.1 --br --write_gamma --gp="102 103 111 112 121"
+#phono3py --fc3 --fc2 --dim="2 2 2" --mesh="9 9 9" -c POSCAR  --sigma 0.1 --br --write_gamma --gp="182 183 184 192 193"
+#phono3py --fc3 --fc2 --dim="2 2 2" --mesh="9 9 9" -c POSCAR  --sigma 0.1 --br --write_gamma --gp="202 273 274 283 364"

docs.it4i/software/chemistry/molpro.md

+# Molpro
+
+Molpro is a complete system of ab initio programs for molecular electronic structure calculations.
+
+## About Molpro
+
+Molpro is a software package used for accurate ab-initio quantum chemistry calculations. More information can be found at the [official webpage](http://www.molpro.net/).
+
+## License
+
+Molpro software package is available only to users that have a valid license. Please contact support to enable access to Molpro if you have a valid license appropriate for running on our cluster (eg. academic research group licence, parallel execution).
+
+To run Molpro, you need to have a valid license token present in " $HOME/.molpro/token". You can download the token from [Molpro website](https://www.molpro.net/licensee/?portal=licensee).
+
+## Installed Version
+
+Currently on Anselm is installed version 2010.1, patch level 45, parallel version compiled with Intel compilers and Intel MPI.
+
+Compilation parameters are default:
+
+| Parameter                          | Value        |
+| ---------------------------------- | ------------ |
+| max number of atoms                | 200          |
+| max number of valence orbitals     | 300          |
+| max number of basis functions      | 4095         |
+| max number of states per symmmetry | 20           |
+| max number of state symmetries     | 16           |
+| max number of records              | 200          |
+| max number of primitives           | maxbfn x [2] |
+
+## Running
+
+Molpro is compiled for parallel execution using MPI and OpenMP. By default, Molpro reads the number of allocated nodes from PBS and launches a data server on one node. On the remaining allocated nodes, compute processes are launched, one process per node, each with 16 threads. You can modify this behavior by using -n, -t and helper-server options. Please refer to the [Molpro documentation](http://www.molpro.net/info/2010.1/doc/manual/node9.html) for more details.
+
+!!! note
+    The OpenMP parallelization in Molpro is limited and has been observed to produce limited scaling. We therefore recommend to use MPI parallelization only. This can be achieved by passing option mpiprocs=16:ompthreads=1 to PBS.
+
+You are advised to use the -d option to point to a directory in [SCRATCH file system - Salomon](/salomon/storage/). Molpro can produce a large amount of temporary data during its run, and it is important that these are placed in the fast scratch file system.
+
+### Example jobscript
+
+```bash
+#PBS -A IT4I-0-0
+#PBS -q qprod
+#PBS -l select=1:ncpus=16:mpiprocs=16:ompthreads=1
+
+cd $PBS_O_WORKDIR
+
+# load Molpro module
+module add molpro
+
+# create a directory in the SCRATCH filesystem
+mkdir -p /scratch/$USER/$PBS_JOBID
+
+# copy an example input
+cp /apps/chem/molpro/2010.1/molprop_2010_1_Linux_x86_64_i8/examples/caffeine_opt_diis.com .
+
+# run Molpro with default options
+molpro -d /scratch/$USER/$PBS_JOBID caffeine_opt_diis.com
+
+# delete scratch directory
+rm -rf /scratch/$USER/$PBS_JOBID
+```

docs.it4i/software/chemistry/nwchem.md

+# NWChem
+
+## Introduction
+
+NWChem aims to provide its users with computational chemistry tools that are scalable both in their ability to treat large scientific computational chemistry problems efficiently, and in their use of available parallel computing resources from high-performance parallel supercomputers to conventional workstation clusters.
+
+[Homepage](http://www.nwchem-sw.org/index.php/Main_Page)
+
+## Installed Versions
+
+For a current list of installed versions, execute:
+
+```console
+$ ml av NWChem
+```
+
+## Running
+
+ NWChem is compiled for parallel MPI execution. Normal procedure for MPI jobs applies. Sample jobscript (for Salomon on 24 threads):
+
+```bash
+#PBS -A IT4I-0-0
+#PBS -q qprod
+#PBS -l select=1:ncpus=24:mpiprocs=24
+
+cd $PBS_O_WORKDIR
+module add NWChem
+mpirun nwchem h2o.nw
+```
+
+## Options
+
+Please refer to [the documentation](http://www.nwchem-sw.org/index.php/Release62:Top-level) and in the input file set the following directives :
+
+* MEMORY : controls the amount of memory NWChem will use
+* SCRATCH_DIR : set this to a directory in [SCRATCH filesystem - Salomon](/salomon/storage/) (or run the calculation completely in a scratch directory). For certain calculations, it might be advisable to reduce I/O by forcing "direct" mode, eg. "scf direct"

docs.it4i/software/chemistry/orca.md

+# ORCA
+
+ORCA is a flexible, efficient and easy-to-use general purpose tool for quantum chemistry with specific emphasis on spectroscopic properties of open-shell molecules. It features a wide variety of standard quantum chemical methods ranging from semiempirical methods to DFT to single- and multireference correlated ab initio methods. It can also treat environmental and relativistic effects.
+
+## ORCA Available on the Cluster
+
+Many versions of ORCA is available on our clusters. You can list all versions by `ml av` command.
+
+```console
+$ ml av orca
+-------------------------- /apps/modules/chem --------------------------
+  ORCA/4_0_0-linux_x86-64    ORCA/4_0_1-linux_x86-64    ORCA/4.0.1.2 (D)
+
+  Where:
+   D:  Default Module
+```
+
+## Serial Computation With ORCA
+
+You can test serial computation with this simple input file. Create a file called orca_serial.inp and fill it with following ORCA commands.
+
+```bash
+    # Taken from the Orca manual
+    # https://orcaforum.cec.mpg.de/OrcaManual.pdf
+    ! HF SVP
+    * xyz 0 1
+      C 0 0 0
+      O 0 0 1.13
+    *
+```
+
+Next, create a PBS submission file (interactive job can be used too).
+
+```bash
+#!/bin/bash
+#PBS -S /bin/bash
+#PBS -N ORCA_SERIAL
+#PBS -l select=1
+#PBS -q qexp
+
+ml ORCA/4.0.1.2
+${EBROOTORCA}orca orca_serial.inp
+```
+
+Submit the job to the queue and wait before it ends. Then you can find an output log in your working directory.
+
+```console
+$ qsub submit_serial.pbs
+1417552.dm2
+
+$ ll ORCA_SERIAL.*
+-rw------- 1 hra0031 hra0031     0 Aug 21 12:24 ORCA_SERIAL.e1417552
+-rw------- 1 hra0031 hra0031 20715 Aug 21 12:25 ORCA_SERIAL.o1417552
+
+$ cat ORCA_SERIAL.o1417552
+
+                                 *****************
+                                 * O   R   C   A *
+                                 *****************
+
+           --- An Ab Initio, DFT and Semiempirical electronic structure package ---
+
+                  #######################################################
+                  #                        -***-                        #
+                  #  Department of molecular theory and spectroscopy    #
+                  #              Directorship: Frank Neese              #
+                  # Max Planck Institute for Chemical Energy Conversion #
+                  #                  D-45470 Muelheim/Ruhr              #
+                  #                       Germany                       #
+                  #                                                     #
+                  #                  All rights reserved                #
+                  #                        -***-                        #
+                  #######################################################
+
+
+                         Program Version 4.0.1.2 - RELEASE -
+
+...
+
+                             ****ORCA TERMINATED NORMALLY****
+TOTAL RUN TIME: 0 days 0 hours 0 minutes 1 seconds 47 msec
+```
+
+## Running ORCA in Parallel
+
+Your serial computation can be converted to parallel in a very simple way. You simply have to specify the number of parallel processes by directive **%pal**. In this example, 4 nodes, 16 cores each are used.
+
+!!! warning
+    Don't use **! PAL** directive as only PAL2 to PAL8 is recognized!
+
+```bash
+    # Taken from the Orca manual
+    # https://orcaforum.cec.mpg.de/OrcaManual.pdf
+    ! HF SVP
+    %pal
+      nprocs 64 # 4 nodes, 16 cores each
+    end
+    * xyz 0 1
+      C 0 0 0
+      O 0 0 1.13
+    *
+```
+
+You also need to edit the previously used PBS submission file. You have to specify number of nodes, cores and MPI-processes to run.
+
+```bash
+#!/bin/bash
+#PBS -S /bin/bash
+#PBS -N ORCA_PARALLEL
+#PBS -l select=4:ncpus=16:mpiprocs=16
+#PBS -q qexp
+
+ml ORCA/4.0.1.2
+${EBROOTORCA}/orca orca_parallel.inp
+```
+
+!!! note
+    When running ORCA in parallel, ORCA should **NOT** be started with mpirun: e.g. mpirun -np 4 orca etc. like many MPI programs and has to be called with full pathname.
+
+Submit this job to the queue and see the output file.
+
+```console
+$ qsub submit_parallel.pbs
+1417598.dm2
+
+$ ll ORCA_PARALLEL.*
+-rw-------  1 hra0031 hra0031     0 Aug 21 13:12 ORCA_PARALLEL.e1417598
+-rw-------  1 hra0031 hra0031 23561 Aug 21 13:13 ORCA_PARALLEL.o1417598
+
+$ cat ORCA_PARALLEL.o1417598
+
+                                 *****************
+                                 * O   R   C   A *
+                                 *****************
+
+           --- An Ab Initio, DFT and Semiempirical electronic structure package ---
+
+                  #######################################################
+                  #                        -***-                        #
+                  #  Department of molecular theory and spectroscopy    #
+                  #              Directorship: Frank Neese              #
+                  # Max Planck Institute for Chemical Energy Conversion #
+                  #                  D-45470 Muelheim/Ruhr              #
+                  #                       Germany                       #
+                  #                                                     #
+                  #                  All rights reserved                #
+                  #                        -***-                        #
+                  #######################################################
+
+
+                         Program Version 4.0.1.2 - RELEASE -
+...
+
+           ************************************************************
+           *        Program running with 64 parallel MPI-processes    *
+           *              working on a common directory               *
+           ************************************************************
+
+...
+                             ****ORCA TERMINATED NORMALLY****
+TOTAL RUN TIME: 0 days 0 hours 0 minutes 11 seconds 859 msec
+```
+
+You can see, that the program was running with 64 parallel MPI-processes. In version 4.0.1.2, only the following modules are parallelized:
+
+* ANOINT
+* CASSCF / NEVPT2
+* CIPSI
+* CIS/TDDFT
+* CPSCF
+* EPRNMR
+* GTOINT
+* MDCI (Canonical-, PNO-, DLPNO-Methods)
+* MP2 and RI-MP2 (including Gradient and Hessian)
+* MRCI
+* PC
+* ROCIS
+* SCF
+* SCFGRAD
+* SCFHESS
+* SOC
+* Numerical Gradients and Frequencies
+
+## Register as a User
+
+You are encouraged to register as a user of ORCA at [Here](https://orcaforum.cec.mpg.de/) in order to take advantage of updates, announcements and also of the users forum.
+
+## Documentation
+
+A comprehensive [PDF](https://orcaforum.cec.mpg.de/OrcaManual.pdf) manual is available online.

docs.it4i/software/chemistry/phono3py.md

+# Phono3py
+
+## Introduction
+
+This GPL software calculates phonon-phonon interactions via the third order force constants. It allows to obtain lattice thermal conductivity, phonon lifetime/linewidth, imaginary part of self energy at the lowest order, joint density of states (JDOS) and weighted-JDOS. For details see Phys. Rev. B 91, 094306 (2015) and [http://atztogo.github.io/phono3py/index.html](http://atztogo.github.io/phono3py/index.html)
+
+Available modules
+
+```console
+$ ml av phono3py
+```
+
+```console
+$ ml phono3py
+```
+
+## Example of Calculating Thermal Conductivity of Si Using VASP Code.
+
+### Calculating Force Constants
+
+One needs to calculate second order and third order force constants using the diamond structure of silicon stored in [POSCAR](POSCAR)  (the same form as in VASP) using single displacement calculations within supercell.
+
+```console
+$ cat POSCAR
+ Si
+   1.0
+     5.4335600309153529    0.0000000000000000    0.0000000000000000
+     0.0000000000000000    5.4335600309153529    0.0000000000000000
+     0.0000000000000000    0.0000000000000000    5.4335600309153529
+ Si
+   8
+Direct
+   0.8750000000000000 0.8750000000000000 0.8750000000000000
+   0.8750000000000000 0.3750000000000000 0.3750000000000000
+   0.3750000000000000 0.8750000000000000 0.3750000000000000
+   0.3750000000000000 0.3750000000000000 0.8750000000000000
+   0.1250000000000000 0.1250000000000000 0.1250000000000000
+   0.1250000000000000 0.6250000000000000 0.6250000000000000
+   0.6250000000000000 0.1250000000000000 0.6250000000000000
+   0.6250000000000000 0.6250000000000000 0.1250000000000000
+```
+
+### Generating Displacement Using 2 by 2 by 2 Supercell for Both Second and Third Order Force Constants
+
+```console
+$ phono3py -d --dim="2 2 2" -c POSCAR
+```
+
+ 111 displacements is created stored in
+disp_fc3.yaml, and the structure input files with this displacements are POSCAR-00XXX, where the XXX=111.
+
+```console
+disp_fc3.yaml POSCAR-00008 POSCAR-00017 POSCAR-00026 POSCAR-00035 POSCAR-00044 POSCAR-00053 POSCAR-00062 POSCAR-00071 POSCAR-00080 POSCAR-00089 POSCAR-00098 POSCAR-00107
+POSCAR         POSCAR-00009 POSCAR-00018 POSCAR-00027 POSCAR-00036 POSCAR-00045 POSCAR-00054 POSCAR-00063 POSCAR-00072 POSCAR-00081 POSCAR-00090 POSCAR-00099 POSCAR-00108
+POSCAR-00001   POSCAR-00010 POSCAR-00019 POSCAR-00028 POSCAR-00037 POSCAR-00046 POSCAR-00055 POSCAR-00064 POSCAR-00073 POSCAR-00082 POSCAR-00091 POSCAR-00100 POSCAR-00109
+POSCAR-00002   POSCAR-00011 POSCAR-00020 POSCAR-00029 POSCAR-00038 POSCAR-00047 POSCAR-00056 POSCAR-00065 POSCAR-00074 POSCAR-00083 POSCAR-00092 POSCAR-00101 POSCAR-00110
+POSCAR-00003   POSCAR-00012 POSCAR-00021 POSCAR-00030 POSCAR-00039 POSCAR-00048 POSCAR-00057 POSCAR-00066 POSCAR-00075 POSCAR-00084 POSCAR-00093 POSCAR-00102 POSCAR-00111
+POSCAR-00004   POSCAR-00013 POSCAR-00022 POSCAR-00031 POSCAR-00040 POSCAR-00049 POSCAR-00058 POSCAR-00067 POSCAR-00076 POSCAR-00085 POSCAR-00094 POSCAR-00103
+POSCAR-00005   POSCAR-00014 POSCAR-00023 POSCAR-00032 POSCAR-00041 POSCAR-00050 POSCAR-00059 POSCAR-00068 POSCAR-00077 POSCAR-00086 POSCAR-00095 POSCAR-00104
+POSCAR-00006   POSCAR-00015 POSCAR-00024 POSCAR-00033 POSCAR-00042 POSCAR-00051 POSCAR-00060 POSCAR-00069 POSCAR-00078 POSCAR-00087 POSCAR-00096 POSCAR-00105
+POSCAR-00007   POSCAR-00016 POSCAR-00025 POSCAR-00034 POSCAR-00043 POSCAR-00052 POSCAR-00061 POSCAR-00070 POSCAR-00079 POSCAR-00088 POSCAR-00097 POSCAR-00106
+```
+
+For each displacement the forces needs to be calculated, i.e. in form of the output file of VASP (vasprun.xml). For a single VASP calculations one needs [KPOINTS](KPOINTS), [POTCAR](POTCAR), [INCAR](INCAR) in your case directory (where you have POSCARS) and those 111 displacements calculations can be generated by [prepare.sh](prepare.sh) script. Then each of the single 111 calculations is submitted [run.sh](run.sh) by [submit.sh](submit.sh).
+
+```console
+$./prepare.sh
+$ls
+disp-00001 disp-00009 disp-00017 disp-00025 disp-00033 disp-00041 disp-00049 disp-00057 disp-00065 disp-00073 disp-00081 disp-00089 disp-00097 disp-00105     INCAR
+disp-00002 disp-00010 disp-00018 disp-00026 disp-00034 disp-00042 disp-00050 disp-00058 disp-00066 disp-00074 disp-00082 disp-00090 disp-00098 disp-00106     KPOINTS
+disp-00003 disp-00011 disp-00019 disp-00027 disp-00035 disp-00043 disp-00051 disp-00059 disp-00067 disp-00075 disp-00083 disp-00091 disp-00099 disp-00107     POSCAR
+disp-00004 disp-00012 disp-00020 disp-00028 disp-00036 disp-00044 disp-00052 disp-00060 disp-00068 disp-00076 disp-00084 disp-00092 disp-00100 disp-00108     POTCAR
+disp-00005 disp-00013 disp-00021 disp-00029 disp-00037 disp-00045 disp-00053 disp-00061 disp-00069 disp-00077 disp-00085 disp-00093 disp-00101 disp-00109     prepare.sh
+disp-00006 disp-00014 disp-00022 disp-00030 disp-00038 disp-00046 disp-00054 disp-00062 disp-00070 disp-00078 disp-00086 disp-00094 disp-00102 disp-00110     run.sh
+disp-00007 disp-00015 disp-00023 disp-00031 disp-00039 disp-00047 disp-00055 disp-00063 disp-00071 disp-00079 disp-00087 disp-00095 disp-00103 disp-00111     submit.sh
+disp-00008 disp-00016 disp-00024 disp-00032 disp-00040 disp-00048 disp-00056 disp-00064 disp-00072 disp-00080 disp-00088 disp-00096 disp-00104 disp_fc3.yaml
+```
+
+Taylor your run.sh script to fit into your project and other needs and submit all 111 calculations using submit.sh script
+
+```console
+$ ./submit.sh
+```
+
+## Collecting Results and Post-Processing With Phono3py
+
+Once all jobs are finished and vasprun.xml is created in each disp-XXXXX directory the collection is done by
+
+```console
+$ phono3py --cf3 disp-{00001..00111}/vasprun.xml
+```
+
+and `disp_fc2.yaml, FORCES_FC2`, `FORCES_FC3` and disp_fc3.yaml should appear and put into the HDF format by
+
+```console
+$ phono3py --dim="2 2 2" -c POSCAR
+```
+
+resulting in `fc2.hdf5` and `fc3.hdf5`
+
+### Thermal Conductivity
+
+The phonon lifetime calculations takes some time, however is independent on grid points, so could be splitted:
+
+```console
+$ phono3py --fc3 --fc2 --dim="2 2 2" --mesh="9 9 9" --sigma 0.1 --wgp
+```
+
+### Inspecting ir_grid_points.yaml
+
+```console
+$ grep grid_point ir_grid_points.yaml
+num_reduced_ir_grid_points: 35
+ir_grid_points:  # [address, weight]
+* grid_point: 0
+* grid_point: 1
+* grid_point: 2
+* grid_point: 3
+* grid_point: 4
+* grid_point: 10
+* grid_point: 11
+* grid_point: 12
+* grid_point: 13
+* grid_point: 20
+* grid_point: 21
+* grid_point: 22
+* grid_point: 30
+* grid_point: 31
+* grid_point: 40
+* grid_point: 91
+* grid_point: 92
+* grid_point: 93
+* grid_point: 94
+* grid_point: 101
+* grid_point: 102
+* grid_point: 103
+* grid_point: 111
+* grid_point: 112
+* grid_point: 121
+* grid_point: 182
+* grid_point: 183
+* grid_point: 184
+* grid_point: 192
+* grid_point: 193
+* grid_point: 202
+* grid_point: 273
+* grid_point: 274
+* grid_point: 283
+* grid_point: 364
+```
+
+one finds which grid points needed to be calculated, for instance using following
+
+```console
+$ phono3py --fc3 --fc2 --dim="2 2 2" --mesh="9 9 9" -c POSCAR  --sigma 0.1 --br --write-gamma --gp="0 1 2
+```
+
+one calculates grid points 0, 1, 2. To automize one can use for instance scripts to submit 5 points in series, see [gofree-cond1.sh](gofree-cond1.sh)
+
+```console
+$ qsub gofree-cond1.sh
+```
+
+Finally the thermal conductivity result is produced by grouping single conductivity per grid calculations using
+
+```console
+$ phono3py --fc3 --fc2 --dim="2 2 2" --mesh="9 9 9" --br --read_gamma
+```

docs.it4i/software/chemistry/prepare.sh

+#!/bin/bash
+P=`pwd`
+
+# number of displacements
+poc=9
+
+        for i in `seq 1 $poc `;
+        do
+        cd $P
+        mkdir disp-0000"$i"
+        cd disp-0000"$i"   
+        cp ../KPOINTS .
+        cp ../INCAR .
+        cp ../POTCAR .
+        cp ../POSCAR-0000"$i" POSCAR
+                echo $i
+        done 
+
+poc=99
+
+        for i in `seq 10 $poc `;
+        do
+        cd $P
+        mkdir disp-000"$i"
+        cd disp-000"$i"
+        cp ../KPOINTS .
+        cp ../INCAR .
+        cp ../POTCAR .
+        cp ../POSCAR-000"$i" POSCAR
+                echo $i
+        done
+poc=111
+
+        for i in `seq 100 $poc `;
+        do
+        cd $P
+        mkdir disp-00"$i"
+        cd disp-00"$i"
+        cp ../KPOINTS .
+        cp ../INCAR .
+        cp ../POTCAR .
+        cp ../POSCAR-00"$i" POSCAR
+                echo $i
+        done

docs.it4i/software/chemistry/run.sh

+#!/bin/bash
+#PBS -A IT4I-9-11
+#PBS -N Si-test
+#PBS -q qprod 
+#PBS -l select=8:ncpus=16:mpiprocs=16:ompthreads=1
+#PBS -l walltime=23:59:59
+##PBS-l mem=6gb
+#PBS -j oe
+#PBS -S /bin/bash
+module load impi/4.1.1.036 intel/13.5.192 fftw3-mpi/3.3.3-icc
+export OMP_NUM_THREADS=1
+export I_MPI_COMPATIBILITY=4
+##export OMP_STACKSIZE=10gb
+b=`basename $PBS_O_WORKDIR`
+echo $b >log.vasp
+SCRDIR=/scratch/$USER/$b
+mkdir -p $SCRDIR
+cd $SCRDIR || exit
+
+# copy input file to scratch 
+cp $PBS_O_WORKDIR/* .
+
+mpirun ~/bin/vasp5.4.1 > log.exc
+
+# copy output file to home
+cp * $PBS_O_WORKDIR/. && cd ..
+
+rm -rf "$SCRDIR"
+
+#exit
+exit

docs.it4i/software/chemistry/submit.sh

+#!/bin/bash
+P=`pwd`
+
+# number of displacements
+poc=9
+
+        for i in `seq 1 $poc `;
+        do
+        cd $P
+        cd disp-0000"$i"   
+        cp ../run.sh .
+        qsub run.sh
+                echo $i
+        done 
+
+poc=99
+
+        for i in `seq 10 $poc `;
+        do
+        cd $P
+        cd disp-000"$i"
+        cp ../run.sh .
+        qsub run.sh
+                echo $i
+        done
+poc=111
+
+        for i in `seq 100 $poc `;
+        do
+        cd $P
+        cd disp-00"$i"
+        cp ../run.sh .
+        qsub run.sh
+                echo $i
+        done

docs.it4i/software/compilers.md

+# Compilers
+
+## Available Compilers
+
+There are several compilers for different programming languages available on the cluster:
+
+* C/C++
+* Fortran 77/90/95/HPF
+* Unified Parallel C
+* Java
+* NVIDIA CUDA (only on Anselm)
+
+The C/C++ and Fortran compilers are provided by:
+
+Open source:
+
+* GNU GCC
+* Clang/LLVM
+
+Commercial licenses:
+
+* Intel
+* PGI
+
+## Intel Compilers
+
+For information about the usage of Intel Compilers and other Intel products, read the [Intel Parallel studio](/software/intel-suite/intel-compilers/) page.
+
+## PGI Compilers (Only on Salomon)
+
+The Portland Group Cluster Development Kit (PGI CDK) is available on Salomon.
+
+```console
+$ ml PGI
+$ pgcc -v
+$ pgc++ -v
+$ pgf77 -v
+$ pgf90 -v
+$ pgf95 -v
+$ pghpf -v
+```
+
+The PGI CDK also incudes tools for debugging and profiling.
+
+PGDBG OpenMP/MPI debugger and PGPROF OpenMP/MPI profiler are available
+
+```console
+$ ml PGI
+$ ml Java
+$ pgdbg &
+$ pgprof &
+```
+
+For more information, see the [PGI page](http://www.pgroup.com/products/pgicdk.htm).
+
+## GNU
+
+For compatibility reasons there are still available the original (old 4.4.7-11) versions of GNU compilers as part of the OS. These are accessible in the search path by default.
+
+It is strongly recommended to use the up to date version which comes with the module GCC:
+
+```console
+$ ml GCC
+$ gcc -v
+$ g++ -v
+$ gfortran -v
+```
+
+With the module loaded two environment variables are predefined. One for maximum optimizations on the cluster's architecture, and the other for debugging purposes:
+
+```console
+$ echo $OPTFLAGS
+-O3 -march=native
+
+$ echo $DEBUGFLAGS
+-O0 -g
+```
+
+For more information about the possibilities of the compilers, see the man pages.
+
+## Unified Parallel C
+
+UPC is supported by two compiler/runtime implementations:
+
+* GNU - SMP/multi-threading support only
+* Berkeley - multi-node support as well as SMP/multi-threading support
+
+### GNU UPC Compiler
+
+To use the GNU UPC compiler and run the compiled binaries use the module GUPC
+
+```console
+$ module add gupc
+$ gupc -v
+$ g++ -v
+```
+
+Simple program to test the compiler
+
+```cpp
+$ cat count.upc
+
+/* hello.upc - a simple UPC example */
+#include <upc.h>
+#include <stdio.h>
+
+int main() {
+  if (MYTHREAD == 0) {
+    printf("Welcome to GNU UPC!!!n");
+  }
+  upc_barrier;
+  printf(" - Hello from thread %in", MYTHREAD);
+  return 0;
+}
+```
+
+To compile the example use
+
+```console
+$ gupc -o count.upc.x count.upc
+```
+
+To run the example with 5 threads issue
+
+```console
+$ ./count.upc.x -fupc-threads-5
+```
+
+For more information see the man pages.
+
+### Berkeley UPC Compiler
+
+To use the Berkeley UPC compiler and runtime environment to run the binaries use the module BUPC
+
+```console
+$ module add BerkeleyUPC/2.16.2-gompi-2015b   # on Anselm: ml bupc
+$ upcc -version
+```
+
+As default UPC network the "SMP" is used. This is very quick and easy way for testing/debugging, but limited to one node only.
+
+For production runs, it is recommended to use the native InfiniBand implementation of UPC network "IBV". For testing/debugging using multiple nodes, the "MPI" UPC network is recommended.
+
+!!! warning
+    Selection of the network is done at the compile time and not at runtime (as expected)!
+
+Example UPC code:
+
+```cpp
+$ cat hello.upc
+
+/* hello.upc - a simple UPC example */
+#include <upc.h>
+#include <stdio.h>
+
+int main() {
+  if (MYTHREAD == 0) {
+    printf("Welcome to Berkeley UPC!!!n");
+  }
+  upc_barrier;
+  printf(" - Hello from thread %in", MYTHREAD);
+  return 0;
+}
+```
+
+To compile the example with the "IBV" UPC network use
+
+```console
+$ upcc -network=ibv -o hello.upc.x hello.upc
+```
+
+To run the example with 5 threads issue
+
+```console
+$ upcrun -n 5 ./hello.upc.x
+```
+
+To run the example on two compute nodes using all 48 cores, with 48 threads, issue (on Anselm compute on 32 cores)
+
+```console
+$ qsub -I -q qprod -A PROJECT_ID -l select=2:ncpus=24
+$ module add bupc
+$ upcrun -n 48 ./hello.upc.x
+```
+
+For more information see the man pages.
+
+## Java
+
+For information how to use Java (runtime and/or compiler), read the [Java page](/software/java/).
+
+## NVIDIA CUDA
+
+For information how to work with NVIDIA CUDA, read the [NVIDIA CUDA page](/anselm/software/nvidia-cuda/).

docs.it4i/software/debuggers/Introduction.md

+# 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](/software/intel/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](/software/debuggers/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](/software/debuggers/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](/software/debuggers/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](/software/debuggers/vampir/) page.

docs.it4i/software/debuggers/aislinn.md

+# 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/](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, 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.
+
+![](../../img/report.png)
+
+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.

docs.it4i/software/debuggers/allinea-ddt.md

+# 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.
+
+![](../../img/ddt1.png)
+
+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)

docs.it4i/software/debuggers/allinea-performance-reports.md

+# 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](/software/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](/salomon/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.

docs.it4i/software/debuggers/cube.md

+# 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.
+
+![](../../img/Snmekobrazovky20141204v12.56.36.png)
+
+\*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 data sets, 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](http://www.scalasca.org/software/cube-4.x/download.html)

docs.it4i/software/debuggers/intel-performance-counter-monitor.md

+# Intel Performance Counter Monitor
+
+## Introduction
+
+Intel PCM (Performance Counter Monitor) is a tool to monitor performance hardware counters on Intel>® processors, similar to [PAPI](/software/debuggers/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 &lt;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](https://software.intel.com/en-us/articles/intel-performance-counter-monitor-a-better-way-to-measure-cpu-utilization])
+1. [Intel® Xeon® Processor E5-2600 Product Family Uncore Performance Monitoring Guide](https://software.intel.com/sites/default/files/m/3/2/2/xeon-e5-2600-uncore-guide.pdf)
+1. [API Documentation](http://intel-pcm-api-documentation.github.io/classPCM.html)

docs.it4i/software/debuggers/intel-vtune-amplifier.md

+# 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.
+
+![](../../img/vtune-amplifier.png)
+
+## 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
+```
+
+!!! warning
+    Temporarily disabled - bug in the kernel ([intel-vtune-amplifier-impact-of-recent-os-security-updates](https://software.intel.com/en-us/articles/intel-vtune-amplifier-impact-of-recent-os-security-updates))
+
+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 (e.g. 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 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. [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)

docs.it4i/software/debuggers/mympiprog_32p_2014-10-15_16-56.html

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+    var radius = cfg.factor*Math.min(cfg.w/2, cfg.h/2);
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+    var g = d3.select(id).append("svg").attr("width", cfg.w).attr("height", 0.75 * cfg.h).append("g");
+
+    var tooltip;
+    function getPosition(i, range, factor, func){
+      factor = typeof factor !== 'undefined' ? factor : 1;
+      return range * (1 - factor * func(i * cfg.radians / total));
+    }
+    function getHorizontalPosition(i, range, factor){
+      return getPosition(i, range, factor, Math.sin);
+    }
+    function getVerticalPosition(i, range, factor){
+      return getPosition(i, range, factor, Math.cos);
+    }
+
+    for(var j=0; j<cfg.levels; j++){
+      var levelFactor = radius*((j+1)/cfg.levels);
+      g.selectAll(".levels").data(allAxis).enter().append("svg:line")
+       .attr("x1", function(d, i){return getHorizontalPosition(i, levelFactor);})
+       .attr("y1", function(d, i){return getVerticalPosition(i, levelFactor);})
+       .attr("x2", function(d, i){return getHorizontalPosition(i+1, levelFactor);})
+       .attr("y2", function(d, i){return getVerticalPosition(i+1, levelFactor);})
+       .attr("class", "line").style("stroke", "grey").style("stroke-width", "0.5px").attr("transform", "translate(" + (cfg.w/2-levelFactor) + ", " + (cfg.h/2-levelFactor) + ")");
+
+    }
+
+    series = 0;
+
+    var axis = g.selectAll(".axis").data(allAxis).enter().append("g").attr("class", "axis");
+
+    axis.append("line")
+        .attr("x1", cfg.w/2)
+        .attr("y1", cfg.h/2)
+        .attr("x2", function(j, i){return getHorizontalPosition(i, cfg.w/2, cfg.factor);})
+        .attr("y2", function(j, i){return getVerticalPosition(i, cfg.h/2, cfg.factor);})
+        .attr("class", "line").style("stroke", "grey").style("stroke-width", "1px");
+
+    axis.append("text").attr("class", function(d){ return "legend_" + d.replace("/", "") })
+        .text(function(d){return d})
+        .style("font-size", cfg.fontSize + "px")
+        .style("text-anchor", function(d, i){
+          var p = getHorizontalPosition(i, 0.5);
+          return (p < 0.4) ? "start" : ((p > 0.6) ? "end" : "middle");
+        })
+        .attr("transform", function(d, i){
+          var p = getVerticalPosition(i, cfg.h / 2);
+          return p < cfg.fontSize ? "translate(0, " + (cfg.fontSize - p) + ")" : "";
+        })
+        .attr("x", function(d, i){return getHorizontalPosition(i, cfg.w / 2, cfg.factorLegend);})
+        .attr("y", function(d, i){return getVerticalPosition(i, cfg.h / 2, cfg.factorLegend);});
+ 
+    d.forEach(function(y, x){
+      dataValues = [];
+      g.selectAll(".nodes")
+        .data(y, function(j, i){
+          dataValues.push([
+            getHorizontalPosition(i, cfg.w/2, (parseFloat(Math.max(j.value, 0))/cfg.maxValue)*cfg.factor),
+            getVerticalPosition(i, cfg.h/2, (parseFloat(Math.max(j.value, 0))/cfg.maxValue)*cfg.factor)
+          ]);
+        });
+      dataValues.push(dataValues[0]);
+      g.selectAll(".area")
+                     .data([dataValues])
+                     .enter()
+                     .append("polygon")
+                     .attr("class", "radar-chart-serie"+series)
+                     .style("stroke-width", "2px")
+                     .style("stroke", cfg.color(series))
+                     .attr("points",function(d) {
+                         var str="";
+                         for(var pti=0;pti<d.length;pti++){
+                             str=str+d[pti][0]+","+d[pti][1]+" ";
+                         }
+                         return str;
+                      })
+                     .style("fill", function(j, i){return cfg.color(series)})
+                     .style("fill-opacity", cfg.opacityArea)
+                     .on('mouseover', function (d){
+                                        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(){
+                                        g.selectAll("polygon").transition(200).style("fill-opacity", cfg.opacityArea);
+                     });
+      series++;
+    });
+    series=0;
+
+
+    d.forEach(function(y, x){
+      g.selectAll(".nodes")
+        .data(y).enter()
+        .append("svg:circle").attr("class", "radar-chart-serie"+series)
+        .attr('r', cfg.radius)
+        .attr("alt", function(j){return Math.max(j.value, 0)})
+        .attr("cx", function(j, i){
+          dataValues.push([
+            getHorizontalPosition(i, cfg.w/2, (parseFloat(Math.max(j.value, 0))/cfg.maxValue)*cfg.factor),
+            getVerticalPosition(i, cfg.h/2, (parseFloat(Math.max(j.value, 0))/cfg.maxValue)*cfg.factor)
+          ]);
+          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, '/&#x200b;'); // 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>&nbsp;<span id="mpi_colrate_units"></span></td><td class="mini_bar_graph"><div id="mpi_colrate_bar" /></td></tr>
+            <tr><td>Effective process point-to-point rate</td><td class="right_cell"><span class="mpi_p2prate_span"><span id="mpi_p2prate_num">0.00e+00</span></span>&nbsp;<span id="mpi_p2prate_units"></span></td><td class="mini_bar_graph"><div id="mpi_p2prate_bar" /></td></tr>
+        </table>
+        <div id="mpi_explanation">
+            <div class="explanation">Most of the time is spent in collective calls with a very low transfer rate. This suggests load imbalance is causing synchonization overhead; use an MPI profiler to investigate further.</div>
+            <div class="explanation"></div>
+        </div>
+    </div>
+    <div class="clear"></div>
+</div>
+<div class="subsections">
+    <div class="ltcol">
+        <div class="heading_io">I/O</div>
+        <div>A breakdown of how the <span class="io_span">0.0</span>% total I/O time was spent:</div>
+        <table id="io_chart">
+            <tr><td>Time in reads</td><td class="right_cell"><span class="io_read_span">0.0</span>%</td><td class="mini_bar_graph"><div id="io_read_bar" /></td></tr>
+            <tr><td>Time in writes</td><td class="right_cell"><span class="io_write_span">0.0</span>%</td><td class="mini_bar_graph"><div id="io_write_bar" /></td></tr>
+            <tr><td>Effective process read rate</td><td class="right_cell"><span class="io_readrate_span"><span id="io_readrate_num">0.00e+00</span></span>&nbsp;<span id="io_readrate_units"></span></td><td class="mini_bar_graph"><div id="io_readrate_bar" /></td></tr>
+            <tr><td>Effective process write rate</td><td class="right_cell"><span class="io_writerate_span"><span id="io_writerate_num">0.00e+00</span></span>&nbsp;<span id="io_writerate_units"></span></td><td class="mini_bar_graph"><div id="io_writerate_bar" /></td></tr>
+        </table>
+        <div id="io_explanation">
+            <div class="explanation">No time is spent in I/O operations. There's nothing to optimize here!</div>
+            <div class="explanation"></div>
+        </div>
+    </div>
+    <div class="rtcol">
+        <div class="heading_ram">Memory</div>
+        <div>Per-process memory usage may also affect scaling:</div>
+        <table id="ram_chart">
+            <tr><td>Mean process memory usage</td><td class="right_cell"><span class="ram_mean_span"><span id="ram_mean_num">2.33e+07</span></span>&nbsp;<span id="ram_mean_units"></span></td><td class="mini_bar_graph"><div id="ram_mean_bar" /></td></tr>
+            <tr><td>Peak process memory usage</td><td class="right_cell"><span class="ram_peak_span"><span id="ram_peak_num">2.35e+07</span></span>&nbsp;<span id="ram_peak_units"></span></td><td class="mini_bar_graph"><div id="ram_peak_bar" /></td></tr>
+            <tr><td>Peak node memory usage</td><td class="right_cell"><span class="ram_node_span">2.8</span>%</td><td class="mini_bar_graph"><div id="ram_node_bar" /></td></tr>
+        </table>
+        <div id="ram_explanation">
+            <div class="explanation">The peak node memory usage is very low. You may be able to reduce the amount of allocation time used by running with fewer MPI processes and more data on each process.</div>
+            <div class="explanation"></div>
+        </div>
+    </div>
+    <div class="clear"></div>
+</div>
+</div> <!-- content -->
+</body>
+</html>

docs.it4i/software/debuggers/mympiprog_32p_2014-10-15_16-56.txt

+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.
+
+

docs.it4i/software/debuggers/papi.md

+# 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](environment-and-modules/) PAPI:
+
+```console
+$ ml papi
+```
+
+This will load the default version. Execute ml av 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](/software/intel/intel-xeon-phi-salomon/) 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/)

docs.it4i/software/debuggers/scalasca.md

+# 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](/software/compilers/) and [OpenMPI](software/mpi/Running_OpenMPI/),
+* scalasca2/2.0-icc-impi, for usage with [Intel Compiler](/software/compilers/) and [Intel MPI](software/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](/software/debuggers/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](/salomon/storage/).
+
+### Analysis of Reports
+
+For the analysis, you must have [Score-P](/software/debuggers/score-p/) and [CUBE](software/debuggers/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 pre-processing is not done and not all metrics will be shown in the viewer.
+
+Refer to [CUBE documentation](/software/debuggers/cube/) on usage of the GUI viewer.
+
+## References
+
+1. [http://www.scalasca.org/](http://www.scalasca.org/)