This is machine translation

Translated by Microsoft
Mouseover text to see original. Click the button below to return to the English version of the page.

Note: This page has been translated by MathWorks. Click here to see
To view all translated materials including this page, select Country from the country navigator on the bottom of this page.

Parallel Computing Toolbox

Perform parallel computations on multicore computers, GPUs, and computer clusters

Parallel Computing Toolbox™ lets you solve computationally and data-intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs—parallel for-loops, special array types, and parallelized numerical algorithms—let you parallelize MATLAB® applications without CUDA or MPI programming. You can use the toolbox with Simulink® to run multiple simulations of a model in parallel.

The toolbox lets you use the full processing power of multicore desktops by executing applications on workers (MATLAB computational engines) that run locally. Without changing the code, you can run the same applications on a computer cluster or a grid computing service (using MATLAB Distributed Computing Server™). You can run parallel applications interactively or in batch.

Getting Started

Learn the basics of Parallel Computing Toolbox

Parallel Computing Fundamentals

Choose a parallel computing solution

Parallel for-Loops (parfor)

Use parallel processing by running parfor on workers in a parallel pool

Asynchronous Parallel Programming

Evaluate functions in the background using parfeval

Big Data Processing

Analyze big data sets in parallel using distributed arrays, tall arrays, datastores, or mapreduce, on Spark® and Hadoop® clusters

Batch Processing

Offload execution of functions to run in the background

GPU Computing

Accelerate your code by running it on a GPU

Clusters and Clouds

Discover cluster resources, and work with cluster profiles.

Performance Profiling

Improve performance of parallel code