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

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

Matlab | |
---|---|

Version: | R2013a,R2015a |

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/R2013a

See the vendor documentation: http://www.mathworks.de/de/help/index.html

# 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

- testmat.lsf
#!/bin/bash # enable your environment, which will use .bashrc configuration in your home directory #BSUB -L /bin/bash # the name of your job showing on the queue system #SUB -J mytest # the following BSUB line specify the queue that you will use, #BSUB -q short # the system output and error message output, %J will show as your jobID #BSUB -o %J.out BSUB -e %J.err # the CPU number that you will collect #BSUB -n 4 # when job finish that you will get # email notification BSUB -u YourEmail@partners.org BSUB -N #enter your working directory cd /home/$USER/TestMatlab # Finally, Start the program /cluster/Lic_Apps/Matlab/R2013a/bin/matlab -nosplash -nodisplay -r main_matlabfile

## 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