Parallel Computing Toolbox - Parallel Computing Support in MATLAB and Simulink Products

Parallel Computing Support in MATLAB and Simulink Products

A growing number of functions, System objects™, and features in several MATLAB^® and Simulink^® products offer the ability to take advantage of parallel computing resources without requiring any extra coding. You can enable this support by simply setting a flag or preference. To take advantage of this functionality on your desktop, you need Parallel Computing Toolbox™. To scale the parallel computing support to larger resources such as computer clusters, you also need MATLAB Parallel Server™.

See the list of products ineligible for use with Parallel Computing Toolbox local workers

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Simulink

Product Name	Support Summary	Additional Resources
Simulink	Ability to run multiple Simulink simulations using `parsim` Ability to run multiple simulations in rapid accelerator mode using `parsim` with prebuilt Simulink models	Documentation: Running Parallel Simulations Example: Parameter Sweep in Rapid Accelerator Mode Video: Running Multiple Simulink Simulations in Parallel (2:29)

Code Generation

Product Name	Support Summary	Additional Resources
MATLAB Coder	Use parfor to run MEX-files generated by MATLAB Coder in parallel	Example: Simulation Acceleration using System Objects, MATLAB Coder and Parallel Computing Toolbox
Embedded Coder	Generating and building code in parallel using model blocks	Blog: Parallel Computing with Simulink: Model Reference Builds Release notes: Embedded Coder
Simulink Coder	Generating and building code in parallel using model blocks	Blog: Parallel Computing with Simulink: Model Reference Builds Release notes: Simulink Coder

Computational Biology

Product Name	Support Summary	Additional Resources
Bioinformatics Toolbox	Ability to distribute pairwise alignments to a computer cluster using functions for progressive alignment of multiple sequences (`multialign`) and pairwise distance between sequences (`seqpdist`)	Documentation: Multiple Sequence Alignment

Control System Design and Analysis

Product Name	Support Summary	Additional Resources
Control System Toolbox	Parallel computing support for tuning fixed-structure control systems with the `looptune` and `systune` commands	Documentation: Speed Up Tuning with Parallel Computing Toolbox Software Release notes: Control System Toolbox
Robust Control Toolbox	Parallel computing support for tuning control systems with the `looptune`, `systune`, and `hinfstruct` commands for robustness against plant variation	Release notes: Robust Control Toolbox
Simulink Control Design	Parallel computing support for frequency response estimation of Simulink models	Documentation: Speeding Up Estimation Using Parallel Computing Example: Speeding Up Frequency Response Estimation Using Parallel Computing Release notes: Simulink Control Design
Simulink Design Optimization	Parallel computing support for estimating model parameters, optimizing system response and sensitivity analysis	Article: Improving Simulink Design Optimization Performance using Parallel Computing (PDF) Documentation: Speeding Up Response Optimization Using Parallel Computing Use Parallel Computing for Sensitivity Analysis Release notes: Simulink Design Optimization Video: Speeding Up Design Optimization (5:11)

Image Processing and Computer Vision

Product Name	Support Summary	Additional Resources
Image Processing Toolbox	Option in Batch Image Processor to improve performance with parallel processing. Set the “Parallel” button in the toolstrip to use this option. Option in `blockproc` function to improve performance of block processing tasks. Set the ‘`UseParallel`’ argument to true to use this option. GPU acceleration for popular image processing functions such as `bwmorph`, `edge`, `imfilter`, `imdilate`, `imerode`, `imopen`, `imclose`, `imtophat`, `imbothat`, `radon`, `iradon`, and `imshow`.	Documentation: Image Processing Toolbox See GPU Computing section for a complete list of GPU-enabled functions
Computer Vision Toolbox	Parallel execution support for functions in bag-of-words workflow including bagOfFeatures, bagOfFeatures.encode, trainImageCategoryClassifier, imageCategoryClassifier, and imageCategoryClassifier.predict	Documentation: Enabling Parallel Support with Computer Vision Toolbox Preferences

Math, Statistics, and Optimization

Product Name	Support Summary	Additional Resources
Global Optimization Toolbox	Simultaneous exploration of local solution space in genetic algorithm, particle swarm, and pattern search solvers. The multistart solver runs the local solver from all starting points and can be run in parallel	Documentation: Global Optimization Toolbox Pattern Search GlobalSearch and MultiStart How to Use Parallel Processing
Model-Based Calibration Toolbox	Parallel computing support for fitting multiple models to experimental data Running of multiple optimizations in parallel	Documentation: Parallel Model Building Parallel Computing for Optimization
Deep Learning Toolbox	Parallel computing support for training and simulation GPU support for training and simulation	Documentation: Parallel Computing on CPUs and GPUs Release notes: Deep Learning Toolbox
Optimization Toolbox	Accelerating gradient estimation in selected constrained nonlinear solvers Support for launching parallel computations from `optimtool` GUI	Documentation: Parallel Computing for Optimization Example: Minimizing an Expensive Optimization Problem Using Parallel Computing Toolbox
Statistics and Machine Learning Toolbox	Parallel execution support using resampling functions: `bootstrap, bootci, jackknife, crossval, lasso, lassoglm, plsregress, sequentialfs, treebagger` Parallel execution support for fitting multiclass support vector machines and other classifiers: `fitcecoc` Support in `RandStream` class for generation of reproducible random streams in parallel across multiple workers through `SubStream` property Parallel execution support for D-optimal design generation functions: `candexch, cordexch, daugment, dcovary, rowexch` Parallel execution support for functions with multiple starting points: `kmeans, kmedoids, nnmf` More than 90 GPU-enabled functions in Statistics and Machine Learning Toolbox, including probability distribution, descriptive statistics, and hypothesis testing	Documentation: Parallel Computing Support for Resampling Methods Parallel Computing Support for Random Number Generation Parallel Computing Support for D-optimal Designs Parallel Computing Support for k-Means Clustering Parallel Computing Support for Nonnegative matrix factorization Statistics and Machine Learning Toolbox Functions with gpuArray Arguments

Signal Processing and Communications

Product Name	Support Summary	Additional Resources
Communications Toolbox	GPU support for a select list of System objects. These System objects execute on GPU (graphics processing unit) to improve performance by reducing simulation time and are among the most commonly used functionality in the product: `comm.gpu.LDPCDecoder comm.gpu.ConvolutionalEncoder comm.gpu.TurboDecoder comm.gpu.ViterbiDecoder comm.gpu.BlockDeinterleaver comm.gpu.BlockInterleaver comm.gpu.ConvolutionalDeinterleaver comm.gpu.ConvolutionalInterleaver comm.gpu.PSKDemodulator comm.gpu.PSKModulator comm.gpu.AWGNChannel` MATLAB Compiler Support for GPU System Objects	Documentation: Simulation Acceleration Using Graphics Processing Units (GPUs) Examples: DVB-S.2 System Simulation Using a GPU-Based LDPC Decoder System Object Using GPUs To Accelerate Turbo Coding Bit Error Rate Simulations End to End System Simulation Acceleration Using GPUs Simulation Acceleration using System Objects, MATLAB^® Coder^™ and Parallel Computing Toolbox
Phased Array System Toolbox	Acceleration of clutter model simulation with `parfor` or GPU	Documentation: Constant Gamma Clutter Simulation on GPU Example: GPU Acceleration of Clutter Simulation
Signal Processing Toolbox	GPU acceleration for `xcorr, xcorr2, fftfilt, xcov,` and `cconv`	Example: Accelerating Correlation with GPUs

Verification and Validation

Product Name	Support Summary	Additional Resources
Simulink Test	Parallel computing support for test execution	See Run Tests Using Parallel Execution.