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docs.it4i/anselm/software/numerical-languages/matlab.md
+ 70
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@@ -92,26 +92,26 @@ r1i0n17$ matlab &
To run matlab in batch mode, write an matlab script, then write a bash jobscript and execute via the qsub command. By default, matlab will execute one matlab worker instance per allocated core.
```bash
#!/bin/bash
#PBS -A PROJECT ID
#PBS -q qprod
#PBS -l select=1:ncpus=16:mpiprocs=16:ompthreads=1
#!/bin/bash
#PBS -A PROJECT ID
#PBS -q qprod
#PBS -l select=1:ncpus=16:mpiprocs=16:ompthreads=1
# change to shared scratch directory
SCR=/scratch/work/user/$USER/$PBS_JOBID
mkdir -p $SCR ; cd $SCR || exit
# change to shared scratch directory
SCR=/scratch/work/user/$USER/$PBS_JOBID
mkdir -p $SCR ; cd $SCR || exit
# copy input file to scratch
cp $PBS_O_WORKDIR/matlabcode.m .
# copy input file to scratch
cp $PBS_O_WORKDIR/matlabcode.m .
# load modules
module load MATLAB/2015a-EDU
# load modules
module load MATLAB/2015a-EDU
# execute the calculation
matlab -nodisplay -r matlabcode > output.out
# execute the calculation
matlab -nodisplay -r matlabcode > output.out
# copy output file to home
cp output.out $PBS_O_WORKDIR/.
# copy output file to home
cp output.out $PBS_O_WORKDIR/.
```
This script may be submitted directly to the PBS workload manager via the qsub command. The inputs and matlab script are in matlabcode.m file, outputs in output.out file. Note the missing .m extension in the matlab -r matlabcodefile call, **the .m must not be included**. Note that the **shared /scratch must be used**. Further, it is **important to include quit** statement at the end of the matlabcode.m script.
@@ -138,40 +138,40 @@ This script creates scheduler object "cluster" of type "local" that starts worke
The last step is to start matlabpool with "cluster" object and correct number of workers. We have 24 cores per node, so we start 24 workers.
```console
parpool(cluster,16);
parpool(cluster,16);
... parallel code ...
... parallel code ...
parpool close
parpool close
```
The complete example showing how to use Distributed Computing Toolbox in local mode is shown here.
```console
cluster = parcluster('local');
cluster
cluster = parcluster('local');
cluster
parpool(cluster,24);
parpool(cluster,24);
n=2000;
n=2000;
W = rand(n,n);
W = distributed(W);
x = (1:n)';
x = distributed(x);
spmd
[~, name] = system('hostname')
W = rand(n,n);
W = distributed(W);
x = (1:n)';
x = distributed(x);
spmd
[~, name] = system('hostname')
T = W*x; % Calculation performed on labs, in parallel.
% T and W are both codistributed arrays here.
end
T;
whos % T and W are both distributed arrays here.
T = W*x; % Calculation performed on labs, in parallel.
% T and W are both codistributed arrays here.
end
T;
whos % T and W are both distributed arrays here.
parpool close
quit
parpool close
quit
```
You can copy and paste the example in a .m file and execute. Note that the parpool size should correspond to **total number of cores** available on allocated nodes.
@@ -183,29 +183,29 @@ This mode uses PBS scheduler to launch the parallel pool. It uses the SalomonPBS
This is an example of m-script using PBS mode:
```console
cluster = parcluster('SalomonPBSPro');
set(cluster, 'SubmitArguments', '-A OPEN-0-0');
set(cluster, 'ResourceTemplate', '-q qprod -l select=10:ncpus=16');
set(cluster, 'NumWorkers', 160);
cluster = parcluster('SalomonPBSPro');
set(cluster, 'SubmitArguments', '-A OPEN-0-0');
set(cluster, 'ResourceTemplate', '-q qprod -l select=10:ncpus=16');
set(cluster, 'NumWorkers', 160);
pool = parpool(cluster, 160);
pool = parpool(cluster, 160);
n=2000;
n=2000;
W = rand(n,n);
W = distributed(W);
x = (1:n)';
x = distributed(x);
spmd
[~, name] = system('hostname')
W = rand(n,n);
W = distributed(W);
x = (1:n)';
x = distributed(x);
spmd
[~, name] = system('hostname')
T = W*x; % Calculation performed on labs, in parallel.
% T and W are both codistributed arrays here.
end
whos % T and W are both distributed arrays here.
T = W*x; % Calculation performed on labs, in parallel.
% T and W are both codistributed arrays here.
end
whos % T and W are both distributed arrays here.
% shut down parallel pool
delete(pool)
% shut down parallel pool
delete(pool)
```
Note that we first construct a cluster object using the imported profile, then set some important options, namely: SubmitArguments, where you need to specify accounting id, and ResourceTemplate, where you need to specify number of nodes to run the job.
@@ -224,28 +224,28 @@ For this method, you need to use SalomonDirect profile, import it using [the sam
This is an example of m-script using direct mode:
```console
parallel.importProfile('/apps/all/MATLAB/2015a-EDU/SalomonDirect.settings')
cluster = parcluster('SalomonDirect');
set(cluster, 'NumWorkers', 48);
parallel.importProfile('/apps/all/MATLAB/2015a-EDU/SalomonDirect.settings')
cluster = parcluster('SalomonDirect');
set(cluster, 'NumWorkers', 48);
pool = parpool(cluster, 48);
pool = parpool(cluster, 48);
n=2000;
n=2000;
W = rand(n,n);
W = distributed(W);
x = (1:n)';
x = distributed(x);
spmd
[~, name] = system('hostname')
W = rand(n,n);
W = distributed(W);
x = (1:n)';
x = distributed(x);
spmd
[~, name] = system('hostname')
T = W*x; % Calculation performed on labs, in parallel.
% T and W are both codistributed arrays here.
end
whos % T and W are both distributed arrays here.
T = W*x; % Calculation performed on labs, in parallel.
% T and W are both codistributed arrays here.
end
whos % T and W are both distributed arrays here.
% shut down parallel pool
delete(pool)
% shut down parallel pool
delete(pool)
```
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