Built-in Parallel Computing Support in MathWorks Products

Key functions in several MathWorks products offer built-in ability to take advantage of parallel computing resources without requiring any extra coding. To take advantage of built-in parallel computing functionality on your multicore desktop, you need Parallel Computing Toolbox. To use this functionality on larger resources such as computer clusters, you need MATLAB Distributed Computing Server in addition to the toolbox.

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
Communications System Toolbox	Option to use Parallel Computing Toolbox with Error Rate Test Console for simulation acceleration without code changes Generation of independent channels on multiple workers using the channel objects `rayleighchan`, `ricianchan`, and `mimochan`, enabling the running of multiple simulations using Parallel Computing Toolbox	Documentation: Run Parallel Simulations Using Parallel Computing Toolbox Software
Embedded Coder	Generating and building code in parallel using model blocks	Release notes: Embedded Coder Blog: Parallel Computing with Simulink: Model Reference Builds
Global Optimization Toolbox	Simultaneous exploration of local solution space in genetic algorithm 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 Documentation: Pattern Search Documentation: GlobalSearch and MultiStart Documentation: How to Use Parallel Processing
Image Processing Toolbox	Option in `blockproc` function to improve performance of block processing tasks. Set the `‘UseParallel’` argument to true to use this option.
Model-Based Calibration Toolbox	Parallel computing support for fitting multiple models to experimental data Running of multiple optimizations in parallel	Documentation: Parallel Model Building Documentation: Parallel Computing for Optimization
Optimization Toolbox	Accelerating gradient estimation in selected constrained nonlinear solvers Support for launching parallel computations from `optimtool` GUI	Documentation: Parallel Computing for Optimization Demo: Minimizing an Expensive Optimization Problem Using Parallel Computing Toolbox
Simulink	Ability to run multiple Simulink simulations using `sim` command with parfor Ability to run multiple simulations in rapid accelerator mode using `parfor` with prebuilt Simulink models	Video demo: Introductory Video Documentation: Running Parallel Simulations Demo: Parameter Sweep in Rapid Accelerator Mode
Simulink Coder	Generating and building code in parallel using model blocks	Release notes: Simulink Coder Blog: Parallel Computing with Simulink: Model Reference Builds
Simulink Control Design	Parallel computing support for frequency response estimation of Simulink models	Release notes: Simulink Control Design Documentation: Speeding Up Estimation Using Parallel Computing Web demo: Speeding Up Frequency Response Estimation Using Parallel Computing
Simulink Design Optimization	Parallel computing support for estimating model parameters and optimizing system response	Release notes: Simulink Design Optimization Documentation: Speeding Up Response Optimization Using Parallel Computing Video demo: Accelerating Parameter Estimation using Parallel Computing Article: Improving Simulink Design Optimization Performance using Parallel Computing (PDF)
Statistics Toolbox	Parallel execution support using resampling functions: `bootstrap`, `bootci`, `jackknife`, `crossval`, `treebagger` Support in `RandStream` class for generation of reproducible random streams in parallel across multiple workers through `SubStream` property	Documentation: Parallel computing support for Resampling Methods Documentation: Parallel computing support for Random number generation
SystemTest	Ability to run test iterations on multiple processors or machines by applying the Distributed option	Paper: The Use of Computing Clusters and Automatic Code Generation to Speed Up Simulation Tasks Webinar: Parallel Computing for Signal Processing using SystemTest Webinar: Distributing a Monte Carlo Test Using SystemTest