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Parallel Computing with Pattern Search

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Parallel Pattern Search Using Parallel Computing with patternsearch Parallel Search Function Implementation Issues in Parallel Pattern Search Parallel Computing Considerations

Parallel Pattern Search

Parallel computing is the technique of using multiple processes or processors on a single problem. The reason to use parallel computing is to speed up computations.

If enabled for parallel computation, the Genetic Algorithm and Direct Search Toolbox solver patternsearch automatically distributes the evaluation of objective and constraint functions associated with the points in a pattern to multiple processes or processors. patternsearch uses parallel computing under the following conditions:

• You have a license for Parallel Computing Toolbox™ software.

• Parallel computing is enabled with matlabpool, a Parallel Computing Toolbox function.

• The following options are set using psoptimset or the Optimization Tool:

  • Cache is 'off' (default)

  • CompletePoll is 'on'

  • Vectorized is 'off' (default)

  • UseParallel is 'always'

When these conditions hold, the solver computes the objective function and constraint values of the pattern search in parallel during a poll.

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Using Parallel Computing with patternsearch

• Using Parallel Computing with Multicore Processors

• Using Parallel Computing with a Multiprocessor Network

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 = psoptimset('UseParallel','always');

  • For Optimization Tool, set Options > User function evaluation > Evaluate fitness and constraint functions > 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

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 = psoptimset('UseParallel','always');

   • For Optimization Tool, set Options > User function evaluation > Evaluate fitness and constraint functions > 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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Parallel Search Function

patternsearch can optionally call a search function at each iteration. The search is done in parallel under the following conditions:

• CompleteSearch is 'on'.

• The search method is not @searchneldermead or custom.

• If the search method is a pattern search function or Latin hypercube search, UseParallel is 'always'.

• If the search method is ga, the search method option structure has UseParallel set to 'always'.

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Implementation Issues in Parallel Pattern Search

Pattern search is implemented in the patternsearch solver by using the Parallel Computing Toolbox function parfor. parfor distributes the evaluation of objective and constraint functions among multiple processes or processors.

The limitations on options, listed in Parallel Pattern Search, arise partly from the limitations of parfor, and partly from the nature of parallel processing:

• Cache is implemented as a persistent variable internally. parfor does not handle persistent variables, because the variable could be set differently at different processors.

• CompletePoll determines whether a poll stops as soon as a better point is found. When searching in parallel with parfor, all evaluations are scheduled at once, and computing continues after all evaluations are returned. It is not easy to halt evaluations once they have been scheduled.

• Vectorized determines whether a pattern is evaluated with one function call. If it is 'on', it is not possible to distribute the evaluation of the function using parfor.

More caveats related to parfor are listed in the Limitations section of the Parallel Computing Toolbox documentation.

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Parallel Computing Considerations

The Improving Performance with Parallel Computing section of the Optimization Toolbox documentation contains information on factors that affect the speed of parallel computations, factors that affect the results of parallel computations, and searching for global optima. Those considerations also apply to parallel computing with pattern search.

Additionally, there are considerations having to do with random numbers that apply to Genetic Algorithm and Direct Search Toolbox functions. Random number sequences in MATLAB are pseudorandom, determined from a "seed," an initial setting. Parallel computations use seeds that are not necessarily controllable or reproducible. For example, there is a default global setting on each instance of MATLAB that determines the current seed for random sequences.

Parallel pattern search does not have reproducible polls when used with MADS, and does not have reproducible searches with MADS, the genetic algorithm, or Latin hypercube search methods.

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Pattern Search Examples: Setting Options

Using the Genetic Algorithm

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