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The following guide describe how to load, configure and use matlab @ CINECA's cluster 

Pre-requisites

To use matlab on CINECA HPC's environment, please check the following pre-requisites:

1. You have a valid account defined on HPC cluster, see "Become a User".
2. You have a valid Matlab license file and the license server (port@hostname) is reachable and readable from the selected HPC cluster. Please ask your IT Dept to follow this guide and contact CINECA's staff. 
   
   

When the aforementioned step has been completed, please inform the CINECA's staff of your account and of the license server's details. Your username and account will be authorize to use: 

1. 1. 1. Your license server for the "standard" MATLAB features (MATLAB, SIMULINK, Distrib_Computing_Toolbox, etc,. etc.)
      2. CINECA's license server for the  MATLAB_Distrib_Comp_Engine (256 licenses available)

Load the MATLAB module 

login to Marconi (Galileo) cluster, and type

[user@node166 ~]$ module load profile/eng autoload matlab/R2018a

Configuration

Configure MATLAB to submit jobs will by default to the cluster rather than submit to the local node or local machine.

Cluster Configuration

Start MATLAB, without desktop.

[user@node166 ~]$ matlab -nodesktop

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

>> configCluster

Local Configuration (Work in Progress)

After having installed MATLAB to your workstation (How to here)

CONFIGURATION – If running MATLAB on your local desktop or laptop machine

Add the MATLAB integration scripts to your MATLAB Path by placing the integration scripts into $MATLAB/toolbox/local.

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

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 cached in MATLAB for future sessions.

Configuring Jobs

Prior to submitting the job, various parameters have to be specified in order to be passed to jobs, such as queue, username, e-mail, etc. 

NOTE:  Any parameters specified using the below workflow will be persistent between MATLAB sessions.

Before specifying any parameters, you will need to obtain a handle to the cluster object.

>> % Get a handle to the cluster
 >> c = parcluster;

You are required to specify an Account Name prior to submitting a job. You can retrieve your Account Name / Bugdet info by using the saldo command

>> % Specify an Account to use for MATLAB jobs

>> c.AdditionalProperties.AccountName = ‘account_name’

>> c.AdditionalProperties.QueueName = 'queue name'

You can specify other additional parameters along with your job.

>> % Specify e-mail address to receive notifications about your job

>> c.AdditionalProperties.EmailAddress = ‘test@foo.com’

>> % Specify the walltime

>> c.AdditionalProperties.WallTime = '00:10:00'

>> % Specify processor cores per node.  Default is 18.

>> c.AdditionalProperties.ProcsPerNode = 18

>> % Turn on the Debug Message.  Default is off (logical 0).

>> c.AdditionalProperties.DebugMessagesTurnedOn = 1

To see the values of the current configuration options, call the specific AdditionalProperties name.

>> % To view current configurations

>> c.AdditionalProperties.QueueName

Or to see the entire configuration

>> c.AdditionalProperties

To clear a value, assign the property an empty value (‘’, [], or false).

>> % To clear a configuration that takes a string as input 
 >> c.AdditionalProperties.EmailAddress = ‘ ’

To save a profile, with your configuration ....

 >> c.saveProfile

Serial Jobs

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

>> j = c.batch(@pwd, 1, {});

>> % Query job for state:  queued | running | finished 

>> j.State

>> % If state is finished, fetch results

>> j.fetchOutputs{:}

>> % Delete the job after results are no longer needed

>> j.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 using 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.

To view results of a previously completed job:

>> % Get a handle on job with job array index 2 (2x1)j

>> j2 = 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

>> j2.fetchOutputs{:}

>> % If the job produces an error view the error log file

>> c.getDebugLog(j.Tasks(1))

NOTE: When submitting independent jobs, with multiple tasks, you will have to specify the task number.

Parallel Jobs

Users can also submit parallel workflows with batch.  Let’s use the following example for a parallel job. 

We will use the batch command again, but since we’re running 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 iterations

>> j = c.batch(@parallel_example, 1, {}, ‘Pool’, 4);

For more info on the batch commands, please see the MATLAB on-line guide.

>> % View current job status

>> j.State

>> % Fetch the results after a finished state is retrieved

>> j.fetchOutputs{:}

ans = 15.5328

The job ran in 15.53 sec. using 4 workers. 

Note that these jobs will always request N+1 cores for each compute node, 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 at a later time, 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

>> j = c.batch(@parallel_example, 1, {}, ‘Pool’, 8);

>> % Get the job ID

>> id = j.ID

Id = 4

>> % Clear workspace, as though we quit MATLAB

>> clear j

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

>> j = c.findJob(‘ID’, 4);

>> % Retrieve the state of the job

>> j.State

ans

finished

>> % Fetch the results

>> j.fetchOutputs{:};

ans =

6.4488

>> % If necessary, retrieve output/error log file

>> c.getDebugLog(j)

The job now runs 6.4488 seconds using 8 workers.  Run code with different number of workers to determine the ideal number to use.

Debugging

If a serial job produces an error, we can call the getDebugLog method to view the error log file.

>> j.Parent.getDebugLog(j.Tasks(1))

When submitting independent jobs, with multiple tasks, you will have to specify the task number.  For Pool jobs, do not deference into the job object.

>> j.Parent.getDebugLog(j)

The scheduler job ID can be derived by calling schedID

>> schedID(j)

ans

25539

To learn More

To learn more about the MATLAB Parallel Computing Toolbox, check out these resources:

• Parallel Computing Coding Examples
• Parallel Computing Documentation
• Parallel Computing Overview
• Parallel Computing Tutorials
• Parallel Computing Videos
• Parallel Computing Webinars

Parallel Computing Benchmarck and Performance

Checking the license server

To check the CINECA's license server status, please type: 

[user@node165]$ module load profile/eng autoload matlab/R2018a

[user@node165]$ lmutil lmstat -a -c $MLM_LICENSE_FILE

Feature enabled: 

Users of MATLAB:  5 licenses issued

Users of Distrib_Computing_Toolbox:  5 licenses issued

Users of MATLAB_Distrib_Comp_Engine:  256 licenses issued

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