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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

**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.

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

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.

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 (Deep Learning Toolbox)**

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

Learn about MATLAB and Parallel Computing Toolbox.

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.

**Run MATLAB Functions in Thread-Based Environment**

Check support for MATLAB functions that you want to run in the background.

**Set Environment Variables on Workers**

Copy system environment variables from the client to workers in a cluster.

Write parallel code that can use parallel resources if you have Parallel Computing Toolbox and that still runs if you do not have Parallel Computing Toolbox.