Statistics and Machine Learning Toolbox™ allows you to use parallel computing to speed up certain statistical computations. In parallel computing, a single MATLAB® client session distributes code segments to multiple workers for independent processing, and then combines these individual results to complete the computation. Use parallel computing to speed up resampling techniques such as bootstrap and jackknife, boosting and bagging of decision trees, cross-validation, clustering algorithms, and more. For a complete list of Statistics and Machine Learning Toolbox functions that support parallel computing, see Function List (Automatic Parallel Support).
Some functions accept
gpuArray (Parallel Computing Toolbox) input arguments so that
you can accelerate code by running on a graphics processing unit (GPU). For the full
list of Statistics and Machine Learning Toolbox functions that accept GPU arrays, see Function List (GPU Arrays).
You must have a Parallel Computing Toolbox™ license to use the parallel computing functionality and GPU arrays.
Get started with parallel statistical computing.
Overview of the ideas in parallel statistical computations.
Deciding when to call functions in parallel.
Parallel computing using
parfor with statistics
Speed up the jackknife using parallel computing.
Speed up cross-validation using parallel computing.
Speed up the bootstrap using parallel computing.
How to obtain identical results from repeated parallel computations.
Accelerate code by using gpuArray input arguments.