This document provides the steps to configure MATLAB to submit non interactive jobs to the HPC cluster, retrieve results, and debug errors.

Thanks to Jonathan Murray from MathWorks for the great support and for compiling the following section.

The MATLAB support package for the cluster can be found as follows:

Windows: unipr.nonshared.r2021b.tar.gz
Linux/macOS: cluster.shared.r2021b.tar.gz

Download the appropriate archive file and start MATLAB. The archive file should be untarred/unzipped in the location returned by calling

 userpath

Configure MATLAB to run parallel jobs on your cluster by calling configCluster. configCluster only needs to be called once per version of MATLAB.

 configCluster

Submission to the remote cluster requires SSH credentials. You will be prompted for your ssh username and password or identity file (private key). The username and location of the private key will be stored in MATLAB for future sessions.

Jobs will now default to the cluster rather than submit to the local machine. NOTE: If you would like to submit to the local machine then run the following command:

 % Get a handle to the local resources
 c = parcluster('local');

Prior to submitting the job, we can specify various parameters to pass to our jobs, such as queue, e-mail, walltime, etc.

configure_cluster.m

   % Get a handle to the cluster
   c = parcluster;
 
   % Specify the walltime (e.g. 5 hours)
   c.AdditionalProperties.WallTime = '05:00:00';
 
   % Specify an account to use for MATLAB jobs
   c.AdditionalProperties.AccountName = 'account-name';
 
   % Specify a queue to use for MATLAB jobs				
   c.AdditionalProperties.QueueName = 'queue-name';
 
   % Specify e-mail address to receive notifications about your job
   c.AdditionalProperties.EmailAddress = 'user-id@unipr.it';
 
   % Specify number of GPUs
   c.AdditionalProperties.GpusPerNode = 1;
 
   % Specify number of nodes to use
   c.AdditionalProperties.Nodes = 1;
 
   % Specify processes per node
   c.AdditionalProperties.ProcsPerNode = 0;
 
   % Specify number of GPUs to use
   c.AdditionalProperties.GpusPerNode = 0;
 
   % Specify type of GPU card to use
   c.AdditionalProperties.GpuCard = '';
 
   % Specify memory to use for MATLAB jobs, per core
   c.AdditionalProperties.MemUsage = '6gb';
 
   % Specify a reservation
   c.AdditionalProperties.Reservation = '';
 
   % Require exclusive node
   c.AdditionalProperties.RequireExclusiveNode = false;

Save changes after modifying AdditionalProperties for the above changes to persist between MATLAB sessions.

 c.saveProfile

To see the values of the current configuration options, display AdditionalProperties.

 % To view current properties
 c.AdditionalProperties

Unset a value when no longer needed.

 % Turn off email notifications 
 c.AdditionalProperties.EmailAddress = '';
 c.saveProfile
 

Use the batch command to submit asynchronous jobs to the cluster. The batch command will return a job object which is used to access the output of the submitted job. See the MATLAB documentation for more help on batch.

 % Get a handle to the cluster
 c = parcluster;

 % Submit job to query where MATLAB is running on the cluster
 job = c.batch(@pwd, 1, {}, 'CurrentFolder','.', 'AutoAddClientPath',false);

 % Query job for state
job.State

% If state is finished, fetch the results
job.fetchOutputs{:}

% Delete the job after results are no longer needed
job.delete

To retrieve a list of currently running or completed jobs, call parcluster to retrieve the cluster object. The cluster object stores an array of jobs that were run, are running, or are queued to run. This allows us to fetch the results of completed jobs. Retrieve and view the list of jobs as shown below.

 c = parcluster;
 jobs = c.Jobs;

Once we’ve identified the job we want, we can retrieve the results as we’ve done previously. fetchOutputs is used to retrieve function output arguments; if calling batch with a script, use load instead. Data that has been written to files on the cluster needs be retrieved directly from the file system (e.g. via ftp). To view results of a previously completed job:

 % Get a handle to the job with ID 2
 job2 = c.Jobs(2);

NOTE: You can view a list of your jobs, as well as their IDs, using the above c.Jobs command.

% Fetch results for job with ID 2
job2.fetchOutputs{:}

Users can also submit parallel workflows with the batch command. Let’s use the following example for a parallel job, which is saved as parallel_example.m.

parallel_example.m

   function [t, A] = parallel_example(iter)
    if nargin==0
      iter = 8;
   end
   disp('Start sim')
   t0 = tic;
   parfor idx = 1:iter
      A(idx) = idx;
      pause(2)
      idx
   end
   t = toc(t0);
   disp('Sim completed')
   save RESULTS A
   end

This time when we use the batch command, to run a parallel job, we’ll also specify a MATLAB Pool.

 % Get a handle to the cluster
 c = parcluster;

 % Submit a batch pool job using 4 workers for 16 simulations
 job = c.batch(@parallel_example, 1, {16}, 'Pool',4, 'CurrentFolder','.', 'AutoAddClientPath',false);

 % View current job status
job.State

 % Fetch the results after a finished state is retrieved
 job.fetchOutputs{:}
ans = 
8.8872

The job ran in 8.89 seconds using four workers. Note that these jobs will always request N+1 CPU cores, since one worker is required to manage the batch job and pool of workers. For example, a job that needs eight workers will consume nine CPU cores.

We’ll run the same simulation but increase the Pool size. This time, to retrieve the results later, we’ll keep track of the job ID. NOTE: For some applications, there will be a diminishing return when allocating too many workers, as the overhead may exceed computation time.

  
 % Get a handle to the cluster
 c = parcluster;

 % Submit a batch pool job using 8 workers for 16 simulations
 job = c.batch(@parallel_example, 1, {16}, 'Pool', 8, 'CurrentFolder','.', 'AutoAddClientPath',false);

 % Get the job ID
 id = job.ID
 
 id = 4
 % Clear job from workspace (as though we quit MATLAB)
 clear job

Once we have a handle to the cluster, we’ll call the findJob method to search for the job with the specified job ID.

 
 % Get a handle to the cluster
 c = parcluster;

 % Find the old job
 job = c.findJob('ID', 4);

 % Retrieve the state of the job
 job.State
 ans = finished
 
 % Fetch the results
 job.fetchOutputs{:};
 
 ans =  4.7270

The job now runs in 4.73 seconds using eight workers. Run code with different number of workers to determine the ideal number to use. Alternatively, to retrieve job results via a graphical user interface, use the Job Monitor (Parallel > Monitor Jobs).

If a serial job produces an error, call the getDebugLog method to view the error log file. When submitting independent jobs, with multiple tasks, specify the task number.

 c.getDebugLog(job.Tasks(3))

For Pool jobs, only specify the job object.

 c.getDebugLog(job)

When troubleshooting a job, the cluster admin may request the scheduler ID of the job. This can be derived by calling schedID

 schedID(job)
 ans =  25539

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