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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

`parfor` | Execute for-loop iterations in parallel on workers in parallel pool |

`parfeval` | Execute function asynchronously on parallel pool worker |

`gpuArray` | Create array 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 |

**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.

Use

**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

Learn about MATLAB and Parallel Computing Toolbox

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.

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