Speed Up Statistical Computations
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.
- Quick Start Parallel Computing for Statistics and Machine Learning Toolbox
Get started with parallel statistical computing.
- Concepts of Parallel Computing in Statistics and Machine Learning Toolbox
Overview of the ideas in parallel statistical computations.
- When to Run Statistical Functions in Parallel
Deciding when to call functions in parallel.
- Working with parfor
Parallel computing using
parforwith statistics functions.
- Implement Jackknife Using Parallel Computing
Speed up the jackknife using parallel computing.
- Implement Cross-Validation Using Parallel Computing
Speed up cross-validation using parallel computing.
- Implement Bootstrap Using Parallel Computing
Speed up the bootstrap using parallel computing.
- Reproducibility in Parallel Statistical Computations
How to obtain identical results from repeated parallel computations.
- Analyze and Model Data on GPU
Accelerate your code by using GPU array input arguments.