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Mathworks MATLAB

MATLAB (matrix laboratory) is a multi-paradigm numerical computing environment and fourth-generation programming language. Developed by MathWorks, MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, Java, and Fortran. (taken from, more details there)

This page currently undergoes a revision.

We currently offer the following MATLAB versions:


You can query the versions yourself with module av |& grep -i matlab.


Load the most recent version available with:

$ module load math/MATLAB

You can also load a particular version with module load math/MATLAB/<version>, e.g. module load math/MATLAB/2017a

See the vendor documentation:

Version:R2017a, 2018b
License:University of Mainz

The currently available toolboxes are

Toolbox Version
Curve Fitting Toolbox3.5.8
Econometrics Toolbox5.1
Fuzzy Logic Toolbox2.4
Image Acquisition Toolbox5.5
Image Processing Toolbox10.3
MATLAB Compiler7.0
MATLAB Compiler SDK6.6
Mapping Toolbox4.7
Optimization Toolbox8.2
Parallel Computing Toolbox6.13
Partial Differential Equation Toolbox3.1
Signal Processing Toolbox8.1
Statistics and Machine Learning Toolbox11.4
Symbolic Math Toolbox8.2
Wavelet Toolbox5.1

Using Matlab

Using Matlab is possible several ways, which we would like to briefly introduce here.

Request resources according to your needs, e.g. with:

[joe_user@login]$ srun -n<ntasks> -p<partition> -A<account> -t<time> --mem<amount> --pty --preserve-env $SHELL  

Load the desired Matlab module:

module load math/MATLAB

Now you can run Matlab. Add -nojvm flag to start Matlab without the Java virtual machine, nosplash prevents Matlab from displaying the Matlab logo.

matlab -nojvm -nosplash

Check the version info and the available toolboxes for that version:

>> ver


MATLAB Version: (R2018b)
Operating System: Linux 3.10.0-957.5.1.el7.x86_64 #1 SMP Fri Feb 1 14:54:57 UTC 2019 x86_64
Java Version: Java is not enabled
MATLAB                                                Version 9.5         (R2018b)
Curve Fitting Toolbox                                 Version 3.5.8       (R2018b)
Econometrics Toolbox                                  Version 5.1         (R2018b)

The use of the Matlab-Compiler is to be preferred!

Only a limited number of licences are available on the cluster. It is therefore preferable to use the Matlab-Compiler to compile your scripts beforehand. We explain more about the Matlab-Compiler as follows.

There are several options to compile your Matlab code to stand-alone executables/libraries. Being independent of licenses is one of the major advantages here, of course. But when running compiled code with the Matlab Runtime Envirenment (MRE) on the cluster you have to consider the threading of your code just as well as when you run Matlab itself. Generally, Matlab detects the number of physical cores and opens the same amount of threads to make full use of the multithreading implemented in the built-in functions. So, if you call

 mcc -m my_mfile.m 

you obtain multithread code. Often this might be wanted, but you have to make sure that you select the appropriate resources for this then - namely, the appropriate core-affinity. Since Matlab wants to use everything on a host you'll have to call bsub -n 1 -R 'affinity[cores(64)]' and an appropriate memory reservation. On the other hand, if your code doesn't need the full multithreading capability, which often is the case, you should compile your code with the flag 'singleCompThread'

 mcc -m -R -singleCompThread my_mfile.m 

. This makes sure that your standalone code will run on a single computational thread, which not only doesn't frustrate the core scheduler and the other users less but improves performance of your code because less time is spent in scheduling all the threads on one core.


The flag nojvm starts Matlab without the Java virtual machine, nodisplay instructs Matlab to run without the GUI, nosplash prevents Matlab from displaying the Matlab logo and r defines the name of the script you want to run.
Notice: Save your file with the .m extension, but call it without the .m extension.

For full nodes consider

#SBATCH -p nodeshort            # Queue name
#SBATCH -n 32                   # Total number of tasks
#SBATCH -c 2                    # Total number of cores

as Matlab will not be able to fully utilize the node, but considers every FPU equal to a CPU.

Using the following shell script you can compile a m-file into a stand-alone c application. It's a variation of the script for using local scratch on a node.
# Store working directory to be safe
# We define a bash function to do the cleaning when the signal is caught
    cp /jobdir/${LSB_JOBID}/*$ ${SAVEDPWD}/
    cp /jobdir/${LSB_JOBID}/$FILENAME ${SAVEDPWD}/
    exit 0
# Register the cleanup function when SIGUSR2 is sent, ten minutes before the job gets killed
trap 'cleanup' SIGUSR2
# Copy input file
cp ${SAVEDPWD}/$FILENAME.m /jobdir/${LSB_JOBID}/
# Go to jobdir and start the program
cd /jobdir/${LSB_JOBID}
mcc -m $FILENAME.m
# Call the cleanup function when everything went fine
  • software/matlab.1568043980.txt.gz
  • Last modified: 2019/09/09 17:46
  • by jrutte02