Parallel Computing Fundamentals

Choose a parallel computing solution

Parallel computing can help you to solve big computing problems in different ways. MATLAB^® and Parallel Computing Toolbox™ provide an interactive programming environment to help tackle your computing tasks. If your code runs too slowly, you can profile it, vectorize it, and use built-in MATLAB parallel computing support. Then you can try to accelerate your code by using parfor on multiple MATLAB workers in a parallel pool. If you have big data, you can scale up using distributed arrays or datastore. You can also execute a task without waiting for it to complete, using parfeval, so that you can carry on with other tasks. You can use different types of hardware to solve your parallel computing problems, including desktop computers, GPUs, clusters, and clouds.

Frequently Viewed Topics

Functions

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Key functions in Parallel Computing Toolbox

`parfor`	Execute `for`-loop iterations in parallel on workers
`parfeval`	Execute function asynchronously on parallel pool worker
`gpuArray`	Array stored on GPU
`distributed`	Access elements of distributed arrays from client
`batch`	Run MATLAB script or function on worker
`parpool`	Create parallel pool on cluster
`ticBytes`	Start counting bytes transferred within parallel pool
`tocBytes`	Read how many bytes have been transferred since calling `ticBytes`

Topics

Basics

Choose a Parallel Computing Solution

Discover the most important functionalities offered by MATLAB and Parallel Computing Toolbox to solve your parallel computing problem.

Run MATLAB Functions with Automatic Parallel Support

Take advantage of parallel computing resources without requiring any extra coding.

Interactively Run a Loop in Parallel Using parfor

Convert a slow for-loop into a faster parfor-loop.

Plot During Parameter Sweep with parfor

This example shows how to perform a parameter sweep in parallel and plot progress during parallel computations.

Scale Up from Desktop to Cluster

This example shows how to develop your parallel MATLAB® code on your local machine and scale up to a cluster.

Run Batch Parallel Jobs

Use batch to offload work from your MATLAB session to run in the background.

Process Big Data in the Cloud

This example shows how to access a large data set in the cloud and process it in a cloud cluster using MATLAB capabilities for big data.

Evaluate Functions in the Background Using parfeval

Break out of a loop early and collect results as they become available.

Run MATLAB Functions on a GPU

Hundreds of functions in MATLAB and other toolboxes run automatically on a GPU if you supply a gpuArray argument.

Train Network in the Cloud Using Automatic Parallel Support

This example shows how to train a convolutional neural network using MATLAB automatic support for parallel training.

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What Is Parallel Computing?

Learn about MATLAB and Parallel Computing Toolbox.

Run Code on Parallel Pools

Learn about starting and stopping parallel pools, pool size, and cluster selection.

Choose Between Thread-Based and Process-Based Environments

With Parallel Computing Toolbox, you can run your parallel code in different parallel environments, such as thread-based or process-based environments.

Related Information

Quick Success with parfor

Featured Examples

Simple Benchmarking of PARFOR Using Blackjack

Benchmarks the parfor construct by repeatedly playing the card game of blackjack, also known as 21. We use parfor to play the card game multiple times in parallel, varying the number of MATLAB® workers, but always using the same number of players and hands.

Open Script

Illustrating Three Approaches to GPU Computing: The Mandelbrot Set

How a simple, well-known mathematical problem, the Mandelbrot Set, can be expressed in MATLAB® code. Using Parallel Computing Toolbox™ this code is then adapted to make use of GPU hardware in three ways:

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Parfeval Blackjack

Uses Parallel Computing Toolbox™ to play the card game of blackjack, also known as 21. We simulate a number of players that are independently playing thousands of hands at a time, and display payoff statistics. This example runs the simulations asynchronously on a parallel pool of workers, using parfeval. In this way, we can update a display of the results as they become available.

Open Script

Parallel Computing Toolbox Documentation

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