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Document under development!

This document is still under development and is being written/edited by RCSG and Mathworks staff.

Table of Contents


There are several ways in which to submit Matlab jobs to a cluster.  This document will cover the various ways to run Matlab compute jobs on the Shared Research Compute clusters, which will include using the Parallel Computing Toolbox (PCT) and the Matlab Distributed Compute Engine (MDCE) to submit many independent tasks and to submit a single task that has parallel components.   Examples are included.


Task Parallel - Multiple independent iterations within your workflow.

Data Parallel - Single program working on a problem across multiple processors.

MDCE - Matlab Distributed Compute Engine.  This is a component of Matlab that allows our clusters to run Matlab jobs that exceed the size of a single compute node (multinode parallel jobs).  it also allows jobs to run even if there are not enough toolbox licenses available for a particular toolbox, so long as the university owns at least one license for the particular toolbox. 

Matlab Task - Individual Matlab compute job.

Matlab Job - Submission from within the Matlab GUI that can contain one or more tasks.

Job - Job submitted via the PBS job scheduler (also called PBS Job).

Interactive Jobs

take from old FAQ

Running Jobs with PBS qsub

Good for jobs that are single processor jobs and do not encounter any toolbox license issues.  Take from old FAQ.

Using MDCE for Task Parallel and Data Parallel Jobs

Using MDCE will allow you to submit multiple jobs with a single job submission (Task Parallel) or submit a single task that is a multiprocessor (and possibly multinode) job.  In order to run this type of job you must first configure Matlab for this type of job submission by following these steps;

One-time Setup

These steps need to be performed only once.  Subsequent runs of Matlab need not repeat these steps.

Configuring Matlab

1.   In your home directory create the MdcsDataLocation/sugar subdirectory.

2.  Load the Matlab 2011a environment:

3.  Run Matlab on the login node:

4.  In Matlab, add the ddd folder to your Matlab path so that Matlab will be able to find the scripts necessary to submit and schedule jobs.

  1. Click on File and then Set Path
  2. Click the Add Folder button
  3. Specify the following folder:

    Error saving pathdef.m

    If Matlab reports that it is unable to save pathdef.m in your current folder, then follow the prompts to select your home folder before saving the file.

5.  Import the cluster configuration for sugar

  1. Click on Parallel and then Manage Configurations
  2. Click on File and then Import
  3. Navigate to /opt/apps/matlab/2011a-scripts and select sugar.
  4. Select sugar configuration and click on Start Validation
    1. All four stages should pass:  Find Resources, Distributed Job, Parallel Job, Matlabpool

      Validation will fail on a busy cluster

      If the cluster is busy such that a job submission must wait before it will run then the validation steps will fail.

If all validation stages succeed, then you are ready to run jobs with MDCE.

Submitting Task Parallel Jobs

The following is an example of a Task Parallel job.  The task-parallel example code, frontDemo, calculates the risk and return based on historical data from a collection of stock prices. The core of the code, calcFrontier, minimizes the equations for a set of returns. In order to parallelize the code, the for loop is converted into a parfor loop.  View the m code here.

To submit the job, copy submitParJobToCluster.m into your working directory, make the necessary modifications for your job environment, and then run the code from within Matlab.  This will submit the job.   The code can be downloaded from here.  An explanation of the code follows:


Job Submission Best Practice

The above is only an example used to illustrate how to submit a job and retrieve the results from within the same Matlab session.  In most cases using job.wait will not be desirable because of the length of time it might take for a job to start running (perhaps several hours).  The best practice is to have your m code write the results to a file, rather than waiting for it to finish and retrieve the results with  job.getAllOutputArguments();

Submitting Data Parallel Jobs

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