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
multiple MATLAB workers in a parallel pool. If you have big data,
you can scale up using distributed arrays or
You can also execute a task without waiting for it to complete, using
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.
|Execute for-loop iterations in parallel on workers in parallel pool|
|Execute function asynchronously on parallel pool worker|
|Create array on GPU|
|Access elements of distributed arrays from client|
|Run MATLAB script or function on worker|
|Create parallel pool on cluster|
|Start counting bytes transferred within parallel pool|
|Read how many bytes have been transferred since calling ticBytes|
Discover the most important functionalities offered by MATLAB and Parallel Computing Toolbox to solve your parallel computing problem.
Take advantage of parallel computing resources without requiring any extra coding.
Convert a slow
for-loop into a
This example shows how to perform a parameter sweep in parallel and plot progress during parallel computations.
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 dataset in the cloud and process it in a cloud cluster using MATLAB capabilities for big data.
Break out of a loop early and collect results as they become available.
Hundreds of functions in MATLAB and other toolboxes run automatically on GPU if you supply a
This example shows how to train a convolutional neural network on CIFAR-10 using MATLAB's built-in support for parallel training.