Main Content

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. To get started, see Quick Start Parallel Computing in MATLAB.


expand all

parforExecute for-loop iterations in parallel on workers
parfevalRun function on parallel pool worker
gpuArrayArray stored on GPU
distributedCreate and access 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
delete (Pool)Shut down parallel pool



Learn More