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.