Clusters and Clouds - MATLAB & Simulink (original) (raw)
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.
Functions
| parcluster | Create cluster object |
|---|---|
| parpool | Create parallel pool on cluster |
| gcp | Get current parallel pool |
| pctconfig | Configure settings for Parallel Computing Toolbox client session or MATLAB Parallel Server workers |
| pctRunOnAll | Run command on client and on all workers in parallel pool |
| parallel.validateProfile | Validate parallel environment profile (Since R2025a) |
|---|---|
| parallel.defaultProfile | Examine or set default cluster profile (Since R2022b) |
| parallel.exportProfile | Export one or more profiles to file |
| parallel.importProfile | Import cluster profiles from file |
| saveProfile | Save modified cluster properties to its current profile |
| saveAsProfile | Save cluster properties to specified profile |
| shutdown | Shut down cloud cluster |
|---|---|
| start | Start cloud cluster |
| wait (cluster) | Wait for cloud cluster to change state |
| partition | Partition parallel pool (Since R2025a) |
|---|
Objects
| parallel.Pool | Parallel pool of workers |
|---|---|
| parallel.Cluster | Access cluster properties and behaviors |
Topics
Cluster Setup
- Discover Clusters and Use Cluster Profiles
Find out how to work with cluster profiles and discover cloud clusters. - Specify Your Parallel Settings
Adjust your parallel settings, and automatically create a parallel pool. - Set Environment Variables on Workers
Copy system environment variables from the client to workers in a cluster.
Cluster and Cloud Applications
- Scale Up from Desktop to Cluster
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. - Scale Up Parallel Code to Large Clusters
Discover options to scale your parallel MATLAB code to use large HPC clusters. - Work with Remote GPUs
This example shows how to run MATLAB® code on multiple remote GPUs in a cluster. (Since R2024a) - Benchmark Your Cluster with the HPC Challenge
This example shows how to evaluate the performance of a compute cluster with the HPC Challenge Benchmark. - Benchmark Cluster Workers
This example shows how to run the MATLAB® benchmark on your cluster workers.
Working with Pool Partitions
- Partition Parallel Pools to Optimize Resource Use
Choose how to tailor pool resources to specific parallel workflows. (Since R2025a) - Partition Pools for Efficient Resource Management in Concurrent Parallel Workflows
This example shows how to use pool partitions to effectively manage and optimize resource allocation in concurrent parallel workflows. (Since R2025a)
Related Information
- Parallel and Cloud (Deep Learning Toolbox)
- Installation (MATLAB Parallel Server)
- Reduce Time to Results with MATLAB Using Parallel Computing
Featured Examples
Scale Up from Desktop to Cluster
Develop your parallel MATLAB® code on your local machine and scale up to a cluster.
Process Big Data in the Cloud
Access a large data set in the cloud and process it in a cloud cluster using MATLAB® capabilities for big data.
Work with Remote GPUs
Run MATLAB® code on multiple remote GPUs in a cluster.
- Since R2024a
- Open Live Script