If your computing task is too big for your local computer, you may want to offload your calculation to networked nodes on a computer cluster. This may be a local cluster or a cluster in the cloud. Changes required to run your MATLAB® code on a cluster are often minimal. Use the MATLAB Job Scheduler (MJS) Profile to exploit computer clusters that have MATLAB Distributed Computing Server™ installed.
parcluster | Create cluster object |
parpool | Create parallel pool on cluster |
gcp | Get current parallel pool |
shutdown | Shut down cloud cluster |
start | Start cloud cluster |
wait (cluster) | Wait for cloud cluster to change state |
parallel.defaultClusterProfile | Examine or set default cluster profile |
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 |
pctconfig | Configure settings for Parallel Computing Toolbox client session |
parallel.Pool | Access parallel pool |
parallel.Cluster | Access cluster properties and behaviors |
pctRunOnAll | Run command on client and all workers in parallel pool |
Discover Clusters and Use Cluster Profiles
Find out how to work with cluster profiles and discover cloud clusters running on Amazon EC2.
Run your application on workers in MATLAB Parallel Cloud™
Scale Up parfor-Loops to Cluster and Cloud
Develop parfor-loops on your
desktop, and scale up to a cluster without changing your code.
Specify Your Parallel Preferences
Specify your preferences, and automatically create a parallel pool.
Program Independent Jobs for a Generic Scheduler
Programming an independent job using a generic scheduler interface to distribute the tasks
Program Communicating Jobs for a Generic Scheduler
How to program a communicating job using the generic scheduler interface