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Parallel Computing Toolbox (parfor)

The MATLAB Parallel Computing Toolbox (PCT) extends the MATLAB language with high-level, parallel-processing features such as parallel for loops, parallel regions, message passing, distributed arrays, and parallel numerical methods. It offers a shared-memory computing environment running on the local cluster profile in addition to your MATLAB client. Moreover, the MATLAB Distributed Computing Server (DCS) scales PCT applications up to the limit of your DCS licenses.

This section illustrates the fine-grained parallelism of a parallel for loop (parfor) in a pool job.

The following examples illustrate a method for submitting a small, parallel, MATLAB program with a parallel loop (parfor statement) as a job to a queue. This MATLAB program prints the name of the run host and shows the values of variables numlabs and labindex for each iteration of the parfor loop.

This method uses the job submission command to submit a MATLAB client which calls the MATLAB batch() function with a user-defined cluster profile.

Prepare a MATLAB pool program in a MATLAB script with an appropriate filename, here named myscript.m:

% FILENAME:  myscript.m

[c name] = system('hostname');
fprintf('SERIAL REGION:  hostname:%s\n', name)
numlabs = parpool('poolsize');
fprintf('        hostname                         numlabs  labindex  iteration\n')
fprintf('        -------------------------------  -------  --------  ---------\n')

parfor i = 1:8
    [c name] = system('hostname');
    name = name(1:length(name)-1);
    fprintf('PARALLEL LOOP:  %-31s  %7d  %8d  %9d\n', name,numlabs,labindex,i)

elapsed_time = toc;        % get elapsed time in parallel loop
[c name] = system('hostname');
name = name(1:length(name)-1);
fprintf('SERIAL REGION:  hostname:%s\n', name)
fprintf('Elapsed time in parallel loop:   %f\n', elapsed_time)

The execution of a pool job starts with a worker executing the statements of the first serial region up to the parfor block, when it pauses. A set of workers (the pool) executes the parfor block. When they finish, the first worker resumes by executing the second serial region. The code displays the names of the compute nodes running the batch session and the worker pool.

Prepare a MATLAB script that calls MATLAB function batch() which makes a four-lab pool on which to run the MATLAB code in the file myscript.m. Use an appropriate filename, here named mylclbatch.m:

% FILENAME:  mylclbatch.m

!echo "mylclbatch.m"


Prepare a job submission file with an appropriate filename, here named myjob.sub:

# FILENAME:  myjob.sub

echo "myjob.sub"

module load matlab


matlab -nodisplay -r mylclbatch

Submit the job as a single compute node with one processor core.

One processor core runs myjob.sub and mylclbatch.m.

Once this job starts, a second job submission is made.

View job status

View results of the job


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                    R2013a ( 64-bit (glnxa64)
                             February 15, 2013

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                hostname                         numlabs  labindex  iteration
                -------------------------------  -------  --------  ---------
PARALLEL LOOP:            4         1          2
PARALLEL LOOP:            4         1          4
PARALLEL LOOP:            4         1          5
PARALLEL LOOP:            4         1          6
PARALLEL LOOP:            4         1          1
PARALLEL LOOP:            4         1          3
PARALLEL LOOP:            4         1          7
PARALLEL LOOP:            4         1          8


Elapsed time in parallel loop:   5.411486

To scale up this method to handle a real application, increase the wall time in the submission command to accommodate a longer running job. Secondly, increase the wall time of myslurmprofile by using the Cluster Profile Manager in the Parallel menu to enter a new wall time in the property SubmitArguments.

For more information about MATLAB Parallel Computing Toolbox:


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