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Matlab

Matlab

Owned by marisa-henze
Last updated: Jul 19, 2022 by Adam Harding

Matlab is currently available for use centrally on the HPC cluster system of the University of Iowa. One may use the Matlab environment by loading the appropriate module. 

While it is possible to run Matlab interactively, which can be especially useful for prototyping a job, please remember that login nodes are a shared resource and are intended for launching jobs or prototyping smaller versions of jobs you intend to run. It is not advisable to use interactive sessions for long-running, compute-intensive jobs unless one uses a qlogin session to do so. 

Batch Matlab Jobs

A batch or serial job is generally run on a single node. The following is a very simple example of a non-parallel batch job using Matlab functions. 

Matlab's Parallel Toolbox does not work well if you set a shell preference in your SGE request. If you otherwise use a #$ -S <shell> designation in your SGE qsub scripts, it is best to remove or comment them when submitting parallel Matlab jobs.

This example demonstrates the -nojvm which disables some of Matlab's features in order to start faster and use less memory. This helps maintain the cluster's performance if you have a large batch of jobs which don't require the disabled features.
Simple Matlab batch job: mat-test.sh
#!/bin/bash
# The name of the job:
#$ -N MatlabTest
# Name of the output log file:
#$ -o matjob.log
# Combining output/error messages into one file:
#$ -j y
# Specifying the Queue
#$ -q UI
# One needs to tell the queue system to use the current directory as the working directory
#$ -cwd
# The command(s) to be executed:
matlab -nodisplay -nodesktop -nojvm -r batch
# Note after -r is the name of the routine or function

Here is the Matlab function you are calling from within your job script: 

Matlab function: batch.m
A = fix(100*rand(5,6));
B = fix(100*rand(6,3));
C = A * B
 < M A T L A B (R) >
                  Copyright 1984-2012 The MathWorks, Inc.
                    R2012b (8.0.0.783) 64-bit (glnxa64)
                              August 22, 2012
 
To get started, type one of these: helpwin, helpdesk, or demo.
For product information, visit www.mathworks.com.
 
C =
       12893       12980       15469
       15263       19329       17603
        7885       16894       17448
       14434       20490       27329
        9235       18735       22408

Parallel Matlab Jobs (or "pool jobs")

The current installation of Matlab uses the Parallel Toolbox, which allows for parallel jobs that can use up to the total number of cores per job. Information on the Parallel Toolkit may be found here: http://www.mathworks.com/help/distcomp/index.html. The following example script can be used to submit a Matlab job to SGE to run on 8 cores (you may use all the cores in a node – which may be more than 8). Note that lines starting with '#$'  are SGE shell commands, whereas '#' symbols denote comments, and the remaining lines are matlab commands. 

There is some overhead to running jobs in parallel, so it can be slower to run on multiple cores if the job is small without sufficiently large loops. The easiest way to make a Matlab program parallel compatible is to use parfor (parallel 'for') loops. A parfor loop can be used when each iteration of the loop is independent of all other iterations. Here is a link to Matlab documentation on parfor loops: http://www.mathworks.com/help/distcomp/getting-started-with-parfor.html

Submit the job using 'qsub parallel-test.sh', assuming the name of the submission script is called parallel-test.sh.

Matlab's Parallel Toolbox requires Java, so if your code uses features of the toolbox, you must omit the -nojvm option when invoking matlab, otherwise your code will quit with an uninformative error. You can verify Java is available and otherwise deliberately quit with a helpful error message by adding this command before code which uses a feature requiring Java:

error(javachk('jvm'))

Example Parallel Job: parallel-test.sh
#!/bin/bash
# The name of the job:
#$ -N test        ## replace 'test' with job name 
# Name of the output log file:
#$ -o matlabTest_parfor.log
# Combining output/error messages into one file ( change y to n for separate files)
#$ -j y
# Specify the parallel environment (PE) and number of cores to use (8):
#$ -pe smp 8
# One needs to tell the queue system to use the current directory as the working directory
#$ -cwd
# The matlab commands to be executed; replace "test" with your function name. 
# -r imediately runs a function without presenting an interactive prompt
/opt/matlab/R2012b/bin/matlab -nodisplay -nodesktop -nosplash -r parafor-test

Within the Matlab script itself, you want to specify the number of cores you wish to reserve. This is done using the 'matlabpool('open', #)' command at the beginning of the file and ending the file with 'matlabpool('close')'.  If you only want to use one core then you can omit the 'matlabpool' commands from your file.

Example Matlab script: parafor-test.m
matlabpool('open',8);
tic
start = tic;
clear A
parfor i = 1:100000;
        A(i) = i;
end
stop = toc(start);
stop
matlabpool('close');

Matlab engine for Python

To install Matlab engine for Python in order to invoke Matlab using matlab.engine from your Python code, see the notes specific to using Python.

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