Main Content

Clusters and Clouds

Discover cluster resources, and work with cluster profiles

If your computing task is too big or too slow for your local computer, you can offload your calculation to a cluster onsite or in the cloud to run your MATLAB® code with minimal changes. Try Parallel > Discover Clusters in the MATLAB toolstrip to find out if you already have a cluster available.

If you already have a cluster with a scheduler, you can integrate MATLAB with it using MATLAB Parallel Server™. Alternatively, if you do not have an existing scheduler, then MATLAB Parallel Server provides MATLAB Job Scheduler.


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parclusterCreate cluster object
parpoolCreate parallel pool on cluster
gcpGet current parallel pool
shutdown Shut down cloud cluster
startStart cloud cluster
wait (cluster)Wait for cloud cluster to change state
parallel.defaultClusterProfileExamine or set default cluster profile
parallel.exportProfileExport one or more profiles to file
parallel.importProfileImport cluster profiles from file
saveProfileSave modified cluster properties to its current profile
saveAsProfileSave cluster properties to specified profile
pctconfigConfigure settings for Parallel Computing Toolbox client session


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parallel.PoolParallel pool of workers
parallel.ClusterAccess cluster properties and behaviors
pctRunOnAllRun command on client and all workers in parallel pool

Examples and How To

Cluster Setup

Discover Clusters and Use Cluster Profiles

Find out how to work with cluster profiles and discover cloud clusters running on Amazon EC2.

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.

Process Big Data in the Cloud

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.

Benchmark Your Cluster with the HPC Challenge

This example shows how to evaluate the performance of a compute cluster with the HPC Challenge Benchmark.

Deep Learning

Scale Up Deep Learning in Parallel and in the Cloud (Deep Learning Toolbox)

Options for deep learning with MATLAB using multiple GPUs, locally or in the cloud.

Deep Learning with MATLAB on Multiple GPUs (Deep Learning Toolbox)

Specify multiple GPUs to use locally or in the cloud for training.

Train Network Using Automatic Multi-GPU Support

This example shows how to use multiple GPUs on your local machine for deep learning training using automatic parallel support.

Use parfor to Train Multiple Deep Learning Networks

This example shows how to use a parfor loop to perform a parameter sweep on a training option.

Use parfeval to Train Multiple Deep Learning Networks

This example shows how to use parfeval to perform a parameter sweep on the depth of the network architecture for a deep learning network and retrieve data during training.

Train Deep Learning Networks in Parallel

This example shows how to run multiple deep learning experiments on your local machine.

Train Network in Parallel with Custom Training Loop

This example shows how to set up a custom training loop to train a network in parallel.

Upload Deep Learning Data to the Cloud

This example shows how to upload data to an Amazon S3 bucket.

Send Deep Learning Batch Job to Cluster

This example shows how to send deep learning training batch jobs to a cluster so that you can continue working or close MATLAB during training.


Specify Your Parallel Preferences

Specify your preferences, and automatically create a parallel pool.

Plugin Scripts for Generic Schedulers

How to use plugin scripts to set up generic schedulers.

Set Environment Variables on Workers

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

Featured Examples