The MATLAB Parallel Computing Toolbox (PCT) lets you solve computationally and data-intensive problems using multicore processors, GPUs, and computer clusters.  

The following is a brief tutorial. More details are given in the pdf attachment to this page

An additional PCT example is available on the rush front-end under /util/academic/matlab/example  

In that directory, the files MyMatlabScript.m and slurmMATLAB contain a simple example of running PCT on the CCR cluster.

  • A "dart-throwing" Monte-Carlo pi calculation piMCserial.m (sequential code):


function [time]=piMCserial(N)
for j=1:N
    x=rand; y=rand;
    if (x^2+y^2)<=1, n=n+1; end


  • A parallelized version is:


function [time]=piMCparallel(N)
for j=(labindex-1)*(N/numlabs)+1:labindex*(N/numlabs)
    x=rand; y=rand;
    if (x^2+y^2)<=1, n=n+1; end
mypi=4*gplus(n)/N; %global sum across cores
time=gop(@max, toc); %global max across cores


Note: The same code is executed at the same time on each worker (labindex). Each worker has its own workspace. The above code is written so that x mat files are stored for x workers: stuff1.mat, stuff2.mat, etc. The final answer is globally summed using the gplus parallel command. The total number of workers (numlabs) is specified by the matlabpool.

  • To initialize a matlabpool across 12 workers, write the following wrapper "runjob.m" for the above code:


matlabpool 12
matlabpool close


  • Write the following SLURM script "slurm-MATLAB-pi" to submit runjob.m, which calls the original code. Make sure that all files and subroutines reside in the same directory. In the script, note the use of "export HOME" to reassign the HOME directory prior to running MatLab. This can boost MatLab performance and avoid file access issues when running multiple simultaneous PCS jobs.


#SBATCH --time=01:00:00
#SBATCH --constraint=CPU-E5645
#SBATCH --nodes=1
#SBATCH --cpus-per-task=1
#SBATCH --ntasks-per-node=12
#SBATCH --mail-type=END
#SBATCH --job-name=MatlabPi
#SBATCH --output=matlab.out
#SBATCH --error=matlab.err

echo "working directory = "$SLURM_SUBMIT_DIR
module load matlab
module list
ulimit -s unlimited

# adjusting home directory reportedly yields a 10x MatLab speedup
# it also avoids potential problem with MatLab when running multiple
# simultaneous PCT jobs

matlab < runjob.m
echo "All Done!"


[user@rush:~]$sbatch slurm-MATLAB-pi

  • After the job finishes, collect your mat files.
  • Note: The above method works as long as all workers are on a single node. To use workers spanning multiple nodes, submit your jobs through the MDCS
  • The files below are from a workshop held on 09/25/2014. The workshop focused on using the MATLAB Parallel Computing Toolbox (PCT) in the SLURM environment.