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You can use any of the Statistics Toolbox™ functions with Parallel Computing Toolbox constructs such as parfor and spmd. However, some functions, such as those with interactive displays, can lose functionality in parallel. In particular, displays and interactive usage are not effective on workers (see Vocabulary for Parallel Computation).
Additionally, the following functions are enhanced to use parallel computing internally. These functions use parfor internally to parallelize calculations.
The following functions for fitting multiclass models for support vector machines and other classifiers are also enhanced to use parallel computing internally.
Methods of the class ClassificationECOC:
Methods of the class CompactClassificationECOC
Methods of the class ClassificationPartitionedECOC
This chapter gives the simplest way to use these enhanced functions in parallel. For more advanced topics, including the issues of reproducibility and nested parfor loops, see the other sections in this chapter.
For information on parallel statistical computing at the command line, enter
help parallelstats
To have a function compute in parallel:
To run a statistical computation in parallel, first set up a parallel environment.
For a multicore machine, enter the following at the MATLAB^{®} command line:
parpool(n)
n is the number of workers you want to use.
Create an options structure with the statset function. To run in parallel, set the UseParallel option to true:
paroptions = statset('UseParallel',true);
Call your function with syntax that uses the options structure. For example:
% Run crossval in parallel cvMse = crossval('mse',x,y,'predfun',regf,'Options',paroptions); % Run bootstrp in parallel sts = bootstrp(100,@(x)[mean(x) std(x)],y,'Options',paroptions); % Run TreeBagger in parallel b = TreeBagger(50,meas,spec,'OOBPred','on','Options',paroptions);
For more complete examples of parallel statistical functions, see Parallel Treebagger and Examples of Parallel Statistical Functions.
After you have finished computing in parallel, close the parallel environment:
delete mypool
To run the example Regression of Insurance Risk Rating for Car Imports Using TreeBagger in parallel:
Set up the parallel environment to use two cores:
mypool = parpool(2) Starting parpool using the 'local' profile ... connected to 2 workers. mypool = Pool with properties: AttachedFiles: {0x1 cell} NumWorkers: 2 Cluster: [1x1 parallel.cluster.Local] SpmdEnabled: 1
Set the options to use parallel processing:
paroptions = statset('UseParallel',true);
Load the problem data and separate it into input and response:
load imports-85; Y = X(:,1); X = X(:,2:end);
Estimate feature importance using leaf size 1 and 1000 trees in parallel. Time the function for comparison purposes:
tic b = TreeBagger(1000,X,Y,'Method','r','OOBVarImp','on',... 'cat',16:25,'MinLeaf',1,'Options',paroptions); toc Elapsed time is 16.696336 seconds.
Perform the same computation in serial for timing comparison:
tic b = TreeBagger(1000,X,Y,'Method','r','OOBVarImp','on',... 'cat',16:25,'MinLeaf',1); % No options gives serial toc Elapsed time is 21.747950 seconds.
Computing in parallel took about 75% of the time of computing serially.