From ScientificComputing
Revision as of 17:14, 14 February 2017 by Urbanb (talk | contribs) (Adds Troubleshooting section)

Jump to: navigation, search

MATLAB's Parallel Computing Toolbox can be used to run suitably-written programs in parallel—on multiple cores at once. For this the newest version of MATLAB that is available is recommended.

For suitable MATLAB programs (such as those containing parfor loops), using the Parallel Computing Toolbox requires two steps

  1. opening a local matlabpool and
  2. telling Euler's bsub scheduler to request multiple cores.

Use the local matlabpool

Use the local matlabpool in your MATLAB program and specify the number of cores (e.g., 4):

matlabpool('local', 4);

when done, close the pool:

matlabpool close;

Note that the local matlabpool is limited to 12 cores in releases up to R2016a (8.7/9.0). From release R2016b (9.1) on, you can use all the cores of Euler nodes.

Submit a Parallel Job

Pass the number of cores (e.g., 4) to bsub's -n argument:

bsub -n 4 -W "1:00" matlab -nodisplay -singleCompThread -r simulation

Note that you must not use the -nojvm argument but you should include the -singleCompThread argument.

Troubleshooting Parallel Jobs

Using parallel pools often results in hard-to-diagnose errors. Many of these errors are related to running several pools at the same time, which is not what MATLAB expects. If you encounter persistent problems starting pools, try to perform one of these commands. Before running them, make sure that you do not have a MATLAB processes running.

  1. Remove the matlab_metadat.mat file in your current working directory.
  2. Remove the $HOME/.matlab/local_cluster_jobs directory.
  3. Remove the entire $HOME/.matlab directory. Warning: Your MATLAB settings on Euler will be lost.