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Matlab is accessible to all HPRC users within the terms of our license agreement. If you have particular concerns about whether specific usage falls within the TAMU HPRC license, please send an email to HPRC Helpdesk. You can start a Matlab session either directly on a login node, through our portal or through the batch system.

Running Matlab on a login node

To be able to use Matlab, the Matlab module needs to be loaded first. This can be done using the following command:

[ NetID@cluster ~]$module load Matlab/R2020b

This will setup the environment for Matlab version R2020b. To see a list of all installed versions, use the following command:

[ NetID@cluster ~]$module spider Matlab

Note: New versions of software become available periodically. Version numbers may change.

To start matlab, use the following command:

[ NetID@cluster ~]$matlab

Depending on your X server settings, this will start either the Matlab GUI or the Matlab command-line interface. To start Matlab in command-line interface mode, use the following command with the appropriate flags:

[ NetID@cluster ~]$matlab -nosplash -nodisplay

By default, Matlab will execute a large number of built-in operators and functions multi-threaded and will use as many threads (i.e. cores) as are available on the node. Since login nodes are shared among all users, HPRC restricts the number of computational threads to 8. This should suffice for most cases. Speedup achieved through multi-threading depends on many factors and in certain cases. To explicitly change the number of computational threads, use the following Matlab command:


This will set the number of computational threads to 4.

To completely disable multi-threading, use the -singleCompThread option when starting Matlab:

[ NetID@cluster ~]$matlab -singleCompThread

Usage on the Login Nodes

Please limit interactive processing to short, non-intensive usage. Use non-interactive batch jobs for resource-intensive and/or multiple-core processing. Users are requested to be responsible and courteous to other users when using software on the login nodes.

The most important processing limits here are:
* ONE HOUR of PROCESSING TIME per login session.

  • EIGHT CORES per login session on the same node or (cumulatively) across all login nodes.

Anyone found violating the processing limits will have their processes killed without warning. Repeated violation of these limits will result in account suspension.
Note: Your login session will disconnect after one hour of inactivity.

Running Matlab through the hprc portal

HPRC provides a portal through which users can start an interactive Matlab GUI session inside a web browser. For more information how to use the portal see our HPRC OnDemand Portal section

Running Matlab through the batch system

To run Matlab simulations on the compute nodes HPRC recommend matlabsubmit

For detailed information on using matlabsubmit see the matlabsubmit page section.

Using Matlab Parallel Toolbox on HPRC Resources

In this section, we will focus on utilizing the Parallel toolbox on HPRC cluster. For a general intro to the Parallel Toolbox see the parallel toolbox section on the Mathworks website. Here we will discuss how to use Matlab Cluster profiles to distribute workers over multiple nodes.

Cluster Profiles

Matlab uses the concept of Cluster Profiles to create parallel pools. When Matlab creates a parallel pool, it uses the cluster profile to determine how many workers to use, how many threads every worker can use, where to store meta-data, as well as some other meta-data. There are two kinds of profiles.

  • local profiles: parallel processing is limited to the same node the Matlab client is running.
  • cluster profiles: parallel processing can span multiple nodes; profile interacts with a batch scheduler (e.g. SLURM on grace).

NOTE: we will not discuss local profiles any further here. Processing using a local profile is exactly the same as processing using cluster profiles.

TAMU HPRC provides a framework, to easily manage and update cluster profiles. The central concept in most of the discussion below is the TAMUClusterProperties object. The TAMUClusterProperties object keeps track of all the properties needed to successfully create a parallel pool. That includes typical Matlab properties, such as the number of Matlab workers requested as well as batch scheduler properties such as wall-time and memory. TAMUClusterProperties.

Importing Cluster Profile

For your convenience, HPRC already created a custom Cluster Profile. Using the profile, you can define how many workers you want, how you want to distribute the workers over the nodes, how many computational threads to use, how long to run, etc. Before you can use this profile you need to import it first. This can be done using by calling the following Matlab function.


This function imports the cluster profile and it creates a directory structure in your scratch directory where Matlab will store meta-information during parallel processing. The default location is /scratch/$USER/MatlabJobs/TAMU\<VERSION, where represents the Matlab version. For example, for Matlab R2021b, it will be /scratch/$USER/MatlabJobs/TAMU2021b

Getting the Cluster Profile Object

To get a TAMUClusterProperties object you can do the following:

>> tp=TAMUClusterProperties;

tp is an object of type TAMUClusterProperties with default values for all the properties. To see all the properties, you can just print the value of tp. You can easily change the values using the convenience methods of TAMUClusterProperties

For example, suppose you have Matlab code and want to use 4 workers for parallel processing.

>> tp=TAMUClusterProperties;
>> tp.workers(4);

Creating a Parallel Pool

To start a parallel pool you can use the HPRC convenience function tamuprofile.parpool. It takes as argument a TAMUClustrerProperties object that specifies all the resources that are requested.

For example:

mypool = tamuprofile.parpool(tp)

This code starts a worker pool using the default cluster profile, with 4 additional workers.

NOTE: only instructions within parfor and spmd blocks are executed on the workers. All other instructions are executed on the client.

NOTE: all variables declared inside the parpool block will be destroyed once the block is finished.

Alternative approach to create parallel pool

Matlab already provides functions to create parallel pools, namely: parcluster() and parpool(). You can use these functions as well, but it will be a more complicated to set all the properties correct ( we will not discuss how to do that here). To create a parallel pool using the basic Matlab functions, you can do the following:

cp = parcluster('TAMU2021b')
% add code to set the number of workers manually.
mypool = parpool(cp);

For convenience, TAMU HPRC also provides a convenience function to return a fully populated parcluster object that can be passed into a Matlab parpool function. See below for an example that creates a pool with 4 workers:

tp = TAMUClusterProperties();
cp = tamuprofile.parcluster();
mypool = parpool(cp)
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