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software:matlab

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 http://en.wikipedia.org/wiki/MATLAB, more details there)

http://www.mathworks.de/products/matlab/

Matlab
Version:R2017a
Lizenz:University of Mainz
Developer:Mathworks
Toolboxes:Curve Fitting, Fuzzy Logic, Image Processing, Mapping, Compiler, Compiler SDK, Optimization, Parallel Computing, Partial Differential Equation, Signal Processing, Statistics and Machine Learning, Symbolic Math, Wavelet

Usage

$ module add software/matlab/R2017a

See the vendor documentation: https://www.mathworks.com/help/matlab/

Using Matlab-Compiler

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.

Examples

Submitting a Matlab job

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.

Compiling a m-file on a node

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.

compileFile.sh
#!/bin/bash
 
# Store working directory to be safe
SAVEDPWD=`pwd`
FILENAME=$1
 
# We define a bash function to do the cleaning when the signal is caught
cleanup(){
    cp /jobdir/${LSB_JOBID}/*$FILENAME.sh ${SAVEDPWD}/
    cp /jobdir/${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
cleanup
software/matlab.txt · Last modified: 2019/04/09 13:53 by meesters