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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 Distributed Computing Server™. Alternatively, if you do not have an existing scheduler, then MATLAB Distributed Computing Server provides MATLAB Job Scheduler (MJS) .


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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.PoolAccess parallel pool
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 dataset in the cloud and process it in a cloud cluster using MATLAB capabilities for big data.

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.

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 for a parameter sweep on the depth of the network architecture.

Upload Deep Learning Data to the Cloud

This example shows how to upload your 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.

Integration Scripts for Generic Schedulers

How to use integration scripts to set up generic schedulers.

Related Information

Featured Examples