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Using Parallel Computing with Multicore Processors Using Parallel Computing with a Multiprocessor Network Testing Parallel Computations

Using Parallel Computing with Multicore Processors

If you have a multicore processor, you might see speedup using parallel processing. You can establish a matlabpool of several parallel workers with a Parallel Computing Toolbox license. For a description of Parallel Computing Toolbox software, and the maximum number of parallel workers, see Product Overview.

Suppose you have a dual-core processor, and wish to use parallel computing:

• Enter

  matlabpool open 2

  at the command line. The 2 specifies the number of processors to use.

• • For command-line use, enter

    options = optimset('UseParallel','always');

  • For Optimization Tool, check Options > Approximated derivatives > Evaluate in parallel.

When you run an applicable solver with options, applicable solvers automatically use parallel computing.

To stop computing optimizations in parallel, set UseParallel to 'never', or set the Optimization Tool not to compute in parallel. To halt all parallel computation, enter

matlabpool close

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Using Parallel Computing with a Multiprocessor Network

If you have multiple processors on a network, use Parallel Computing Toolbox functions and MATLAB^® Distributed Computing Server™ software to establish parallel computation. Here are the steps to take:

1. Make sure your system is configured properly for parallel computing. Check with your systems administrator, or refer to the Parallel Computing Toolbox documentation, or MATLAB Distributed Computing Server System Administrator's Guide.

   To perform a basic check:

   1. At the command line enter

      matlabpool open conf

      or

      matlabpool open conf n

      where conf is your configuration, and n is the number of processors you wish to use.

   2. If network_file_path is the network path to your objective or constraint functions, enter

      pctRunOnAll('addpath network_file_path')

      so the worker processors can access your objective or constraint M-files.

   3. Check whether an M-file is on the path of every worker by entering

      pctRunOnAll('which filename')

      If any worker does not have a path to the M-file, it reports

      filename not found.

2. • For command-line use, enter

     options = optimset('UseParallel','always');

   • For Optimization Tool, check Options > Approximated derivatives > Evaluate in parallel.

Once your parallel computing environment is established, applicable solvers automatically use parallel computing whenever called with options.

To stop computing optimizations in parallel, set UseParallel to 'never', or set the Optimization Tool not to compute in parallel. To halt all parallel computation, enter

matlabpool close

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Testing Parallel Computations

To test see if a problem runs correctly in parallel,

1. Try your problem without parallel computation to ensure that it runs properly serially. Make sure this is successful (gives correct results) before going to the next test.

2. Set UseParallel to 'always', and ensure matlabpool is closed. Your problem runs parfor serially, with loop iterations in reverse order from a for loop. Make sure this is successful (gives correct results) before going to the next test.

3. Set UseParallel to 'always', and open matlabpool. Unless you have a multicore processor or a network set up, you won't see any speedup. This testing is simply to verify the correctness of the computations.

Remember to call your solver using an options structure to test or use parallel functionality.

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Parallel Computing in Optimization Toolbox Functions

Improving Performance with Parallel Computing

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