The HPC offers standard MATLAB and a variety of specialized toolboxes. We have license for parallel features that enable a MATLAB job to execute on multiple cores for computationally and data-intensive problems.

Set your MATLAB parallel environment

(Needs to to be done only once by the user)

#Note#: Jobs submitted via the GUI will terminate once the GUI session ends. It is recommended that you submit your MATLAB jobs using the command line batch submission (see below) if you expect the job to take a long time.


Use the visualization system to open a remote desktop session: Visualization Guide

1. Open a terminal in the remote desktop session


2. Type the commands in the terminal to start MATLAB

module load matlab
matlab &


3. Configure the parallel computing toolbox to run jobs on the Panther cluster

a. Environment -> Parallel computing options -> Manage Cluster Profiles -> Import

b. Select the setting file /home/share/matlab/Panther.settings


c. Select Panther cluster to run parallel jobs

Environment -> Parallel computing options -> Current Cluster -> Panther


Step 3 needs to be done only once.
This sets MATLAB to run parallel functions such as parfor, which will be scheduled to run on up to 12 cores in the Panther cluster. Please contact for running a parallel job on more than 12 cores.


User Guide – Command line batch submission

1. Move to your working directory

  • cd working-directory

2. Load the MATLAB module

  • module load matlab

3. Start MATLAB

  • matlab -nodisplay

4. Submit your batch MATLAB job; where myscript is your MATLAB script and  # is 1 less than the number of cores requested for a parallel job. For example, if you require 8 cores then replace # with 7

  • myJob=batch(‘myscript’, ‘Pool’,#); 

Note: You would need to have imported the Panther setting at least once for this to work. See the Set your MATLAB parallel environment section above.

5. Exit MATLAB

  • exit

6. Monitor jobs

  • bjobs



List of licensed toolboxes

  • Curve fitting
  • Control System
  • Image Acquisition
  • Instrument Control
  • Image Processing
  • Partial Differential Equation
  • Signal Processing
  • Statistics
  • Parallel computing