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

parforExecute for-loop iterations in parallel on workers in parallel pool
parfevalExecute function asynchronously on parallel pool worker
gpuArrayCreate array on GPU
distributedAccess elements of distributed arrays from client
batchRun MATLAB script or function on worker
parpoolCreate parallel pool on cluster
ticBytesStart counting bytes transferred within parallel pool
tocBytesRead how many bytes have been transferred since calling ticBytes

Examples and How To

Choose a Parallel Computing Solution

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

Interactively Run a Loop in Parallel Using parfor

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

Run Batch Parallel Jobs

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

Evaluate Functions in the Background Using parfeval

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

Identify and Select a GPU Device

Use gpuDevice to identify and select which device you want to use.

Create and Use Distributed Arrays

When your data array is too big to fit into the memory of a single machine, you can create a distributed array

Concepts

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.

Scale Up parfor-Loops to Cluster and Cloud

Develop parfor-loops on your desktop, and scale up to a cluster without changing your code.

Glossary

Related Information

Featured Examples

Simple Benchmarking of PARFOR Using Blackjack

Simple Benchmarking of PARFOR Using Blackjack

This example 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

Illustrating Three Approaches to GPU Computing: The Mandelbrot Set

This example shows 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:
Open Script
Parfeval Blackjack

Parfeval Blackjack

This example 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.
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