Main Content

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—enable you to parallelize MATLAB® applications without CUDA or MPI programming. The toolbox lets you use parallel-enabled functions in MATLAB and other toolboxes. You can use the toolbox with Simulink® to run multiple simulations of a model in parallel. Programs and models can run in both interactive and batch modes.

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 clusters or clouds (using MATLAB Parallel Server™). You can also use the toolbox with MATLAB Parallel Server to execute matrix calculations that are too large to fit into the memory of a single machine.

Get 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