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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 Toolbox | Option to use Parallel Computing Toolbox with Error Rate Test Console for simulation acceleration without code changes |
Documentation: Attaching a System to the Error Rate Test Console and Running Simulations (Running Simulations) |
| Global Optimization Toolbox | Simultaneous exploration of local solution space in genetic algorithm and pattern search solvers | Documentation: Global Optimization Toolbox Documentation: Pattern Search Demo: Using Genetic Algorithm with Parallel Computing Toolbox |
| 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 |
| Real-Time Workshop | Generating and building code in parallel using model blocks | Release notes: Real-Time Workshop Blog: Parallel Computing with Simulink: Model Reference Builds |
| Real-Time Workshop Embedded Coder | Generating and building code in parallel using model blocks | Release notes: Real-Time Workshop Embedded Coder Blog: Parallel Computing with Simulink: Model Reference Builds |
| 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 |
Documentation: Running Parallel Simulations Release slides: Latest Features in Simulink |
| 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 |
