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Interactively Run a Loop in Parallel Using parfor

In this example, you start with a slow for-loop, and you speed up the calculation using a parfor-loop instead. parfor splits the execution of for-loop iterations over the workers in a parallel pool.

This example calculates the spectral radius of a matrix and converts a for-loop into a parfor-loop. Find out how to measure the resulting speedup.

  1. In the MATLAB® Editor, enter the following for-loop. Add tic and toc to measure the time elapsed.

    tic
    n = 200;
    A = 500;
    a = zeros(n);
    for i = 1:n
        a(i) = max(abs(eig(rand(A))));
    end
    toc
  2. Run the script, and note the elapsed time.

    Elapsed time is 31.935373 seconds.

  3. In the script, replace the for-loop with a parfor-loop.

    tic
    ticBytes(gcp);
    n = 200;
    A = 500;
    a = zeros(n);
    parfor i = 1:n
        a(i) = max(abs(eig(rand(A))));
    end
    tocBytes(gcp)
    toc

  4. Run the new script, and run it again. Note that the first run is slower than the second run, because the parallel pool takes some time to start and make the code available to the workers. Note the elapsed time for the second run.

    By default, MATLAB automatically opens a parallel pool of workers on your local machine.

    Starting parallel pool (parpool) using the 'local' profile ... connected to 4 workers.
    ...
    Elapsed time is 10.760068 seconds. 
    The parfor run on four workers is about three times faster than the corresponding for-loop run. The speed-up is smaller than the ideal speed-up of a factor of four on four workers. This is due to parallel overhead, including the time required to transfer data from the client to the workers and back. This example shows a good speed-up with relatively small parallel overhead, and benefits from conversion into a parfor-loop. Not all for-loop iterations can be turned into faster parfor-loops. To learn more, see Decide When to Use parfor.

One key requirement for using parfor-loops is that the individual iterations must be independent. Independent problems suitable for parfor processing include Monte Carlo simulations and parameter sweeps. For next steps, see Convert for-Loops Into parfor-Loops.

In this example, you managed to speed up the calculation by converting the for-loop into a parfor-loop on four workers. You might reduce the elapsed time further by increasing the number of workers in your parallel pool, see Scale Up parfor-Loops to Cluster and Cloud.

You can modify your cluster profiles to control how many workers run your loops, and whether the workers are local or on a cluster. For more information on profiles, see Discover Clusters and Use Cluster Profiles.

Modify your parallel preferences to control whether a parallel pool is created automatically, and how long it remains available before timing out. For more information on preferences, see Specify Your Parallel Preferences.

You can run Simulink® models in parallel with the parsim command instead of using parfor-loops. For more information and examples of using Simulink in parallel, see Run Multiple Simulations (Simulink).

See Also

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