Use MATLAB on NJIT HPC

Warning

Please note that the following instructions are applicatible for Lochness only. We will soon update the instructions for Wulver.

Installation steps of MATLAB on local machine

• Go to Mathworks Download and register with your NJIT email address.
• Select the MATLAB version installed on Wulver.
• User needs to select the correct installer based on the OS (Mac or Windows).
• Run the installer.

• Make sure to check Parallel Computing Toolbox option.

• Continue by selecting Next and MATLAB will be installed on your computer.

Setup Slurm profile to run MATLAB on Lochness

Following this procedure a user will be able to submit jobs on Wulver from Matlab running locally on the user's computer.

Installing the Add-On

Download the Slurm-Add-on and double-click on it to install. Make sure to keep the MATLAB window open while installing it.

Creating a Profile for Wulver

The following steps will create a profile for lochness (or stheno). Click Next to begin.

In the "Operating System" screen Unix is already selected. Click Next to continue.

This "Submission Mode" screen determines whether to use a shared or nonshared submission mode. Since Matlab installed on your personal computer or laptop does not use a shared job location storage, select "No" where indicated and click Next to continue.

Click Next to continue.

In the Connection Details screen, enter the cluster host as wulver.njit.edu. Enter your UCID for the username. Select No for the Do you want to use an identity file to log in to the cluster option and click next to continue.

In the Cluster Details screen enter the full path to the directory on lochness to store the Matlab job files. In the case the directory is $HOME/MDCS. MDCS stands for Matlab Distributed Computing Server. It is not necessary to name this directory as MDCS. This directory can be named anything you wish. To determine the value of $HOME, connect to Wulver and run the following:

  login-1-45 ~ >: echo $HOME
  /home/g/guest24

Make sure to check the box Use unique subfolders . Click Next to continue.

In the Workers, enter 512 for the number of workers. For MATLAB installation folders for workers, use module av MATLAB command first.

  login-1-45 ~ >: module av MATLAB
  ------------------------------------/apps/easybuild/modules/all/Core---------------------------------------------------------
   MATLAB/2023a

Use "module spider" to find all possible modules and extensions.
Use "module keyword key1 key2 ..." to search for all possible modules matching any of the "keys".

This will show you the list of MATLAB versions installed on Wulver. Next, use module show MATLAB/2023a to check MATLAB installation path.

   login-1-45 ~ >: module show MATLAB/2023a
---------------------------------------------------------------------------------------------------------------------------------
   /apps/easybuild/modules/all/Core/MATLAB/2023a.lua:
---------------------------------------------------------------------------------------------------------------------------------
help([[
Description
===========
The MATLAB Parallel Server Toolbox.


More information
================
 - Homepage: https://www.mathworks.com/help/matlab/matlab-engine-for-python.html
]])
whatis("Description: The MATLAB Parallel Server Toolbox.")
whatis("Homepage: https://www.mathworks.com/help/matlab/matlab-engine-for-python.html")
whatis("URL: https://www.mathworks.com/help/matlab/matlab-engine-for-python.html")
conflict("MATLAB")
prepend_path("CMAKE_PREFIX_PATH","/apps/easybuild/software/MATLAB/R2023a")
prepend_path("PATH","/apps/easybuild/software/MATLAB/R2023a/bin")
setenv("EBROOTMATLAB","/apps/easybuild/software/MATLAB/R2023a")
setenv("EBVERSIONMATLAB","R2023a")
setenv("EBDEVELMATLAB","/apps/easybuild/software/MATLAB/R2023a/easybuild/Core-MATLAB-2023a-easybuild-devel")
prepend_path("PATH","/apps/easybuild/software/MATLAB/R2023a/toolbox/parallel/bin")
prepend_path("PATH","/apps/easybuild/software/MATLAB/R2023a")
prepend_path("LD_LIBRARY_PATH","/apps/easybuild/software/MATLAB/R2023a/runtime/glnxa64")
prepend_path("LD_LIBRARY_PATH","/apps/easybuild/software/MATLAB/R2023a/bin/glnxa64")
prepend_path("LD_LIBRARY_PATH","/apps/easybuild/software/MATLAB/R2023a/sys/os/glnxa64")
setenv("_JAVA_OPTIONS","-Xmx2048m")

The MATLAB installation path is defined by the EBROOTMATLAB environment variable, which, in the above example, is set to /apps/easybuild/software/MATLAB/R2023a. Mention that path in MATLAB installation folders for workers and click Next to continue.

In the "License" screen make sure to select "Network license manager" and click Next to continue.

In the Profile Details screen enter "Wulver". The "Cluster description" is optional and may be left blank. Click Next to continue.

In the Summary screen make sure everything is correct and click Create.

In the Profile Created Successfully screen, check the Set the new profile as default box and click on Finish.

Submitting a Serial Job

This section will demonstrate how to create a cluster object and submit a simple job to the cluster. The job will run the 'hostname' command on the node assigned to the job. The output will indicate clearly that the job ran on the cluster and not on the local computer.

The hostname.m file used in this demonstration can be downloaded here.

 >> c=parcluster 

Certain arguments need to be passed to SLURM in order for the job to run properly. Here we will set values for partion, mem-per-cpu and time. In the Matlab window enter:

 >> c.AdditionalProperties.AdditionalSubmitArgs=['--partition=general --qos=standard --account=PI_UCID --mem-per-cpu=4G --time=2-00:00:00'] 

Replace PI_UCID with the UCID of PI. Check the SLURM Documentation for other SLURM parameters. To make this persistent between Matlab sessions these arguments need to be saved to the profile. In the Matlab window enter:

 >> c.saveProfile 

We will now submit the hostname.m function to the cluster. In the Matlab window enter the following:

>> j=c.batch(@hostname, 1, {}, 'AutoAddClientPath', false); 

• @: Submitting a function.

• 1: The number of output arguments from the evaluated function.

• {}: Cell array of input arguments to the function. In this case empty.

• 'AutoAddClientPath', false: The client path is not available on the cluster.

When the job is submitted, you will be prompted for your password.

For more information see the Mathworks page: batch

To wait for the job to finish, enter the following in the Matlab window:

 >>j.wait

Finally, to get the results:

 >>fetchOutputs(j)

As can be seen, this job ran on node720

Submitting a Parallel Function

The Job Monitor is a convenient way to monitor jobs submitted to the cluster. In the Matlab window select Parallel and then Monitor Jobs.

For more information see the Mathworks page: Job Monitor.

Here we will submit a simple function using a "parfor" loop. The code for this example is as follows:

function t = parallel_example

t0 = tic;
parfor idx = 1:16
        A(idx) = idx;
        pause (2)
end

t=toc(t0);

To submit this job:

 >> j=c.batch(@parallel_example, 1, {}, 'AutoAddClientPath', false, 'Pool', 7)

Since this is a parallel job a 'Pool' must be started. The actual number of tasks started will be one more than requested in the pool. In this case, the batch command calls for a pool of seven. Eight tasks will be started on the cluster.

Also see that the state of the job in the Job Monitor is running.

The job takes a few minutes to run and the state of the job changes to "finished."

Once again to get the results enter:

 >> fetchOutputs(j) 

As can be seen the parfor loop was completed in 6.7591 seconds.

Submitting a Script Requiring a GPU

In this section we will submit a matlab script using a GPU. The results will be written to the job diary. The code for this example is as follows:

% MATLAB script that defines a random matrix and does FFT
%
% The first FFT is without a GPU
% The second is with the GPU
%
% MATLAB knows to use the GPU the second time because it
%   is passed a type gpuArray as an argument to FFT
% We do the FFT a bunch of times to make using the GPU worth it,
%   or else it spends more time offloading to the GPU
%   than performning the calculation
%
% This example is meant to provide a general understanding
%   of MATLAB GPU usage
% Meaningful performance measurements depend on many factors
%   beyond the scope of this example
% Downloaded from https://projects.ncsu.edu/hpc/Software/examples/matlab/gpu/gpu_m

% Define a matrix
A1 = rand(3000,3000);

% Just use the compute node, no GPU
tic;
% Do 1000 FFT's
for i = 1:1000
      B2 = fft(A1);
end
time1 = toc;
fprintf('%s\n',"Time to run FFT on the node:")
disp(time1);

% Use GPU
tic;
A2 = gpuArray(A1);
% Do 1000 FFT's
for i = 1:1000
      % MALAB knows to use GPU FFT because A2 is defined by gpuArray
        B2 = fft(A2);
end
time2 = toc;
fprintf('%s\n',"Time to run FFT on the GPU:")
disp(time2);

% Will be greater than 1 if GPU is faster
speedup = time1/time2 

We will need to change the partition to gpu to request a gpu. In the Matlab window enter:

 >> c.AdditionalProperties.AdditionalSubmitArgs=['--partition=gpu --qos=standard --account=PI_UCID --gres=gpu:1 --mem-per-cpu=4G --time=2-00:00:00'] 

Submit the job as before. Since a script is submitted as opposed to a function, only the name of the script is included in the batch command. Do not include the '@' symbol. In a script there are no inputs or ouptuts.

 >> j=c.batch('gpu', 'AutoAddClientPath', false) 

To get the result:

 >> j.diary 

Load and Plot Results from A Job

In this section we will run a job on the cluster and then load and plot the results in the local Matlab workspace. The code for this example is as follows:

n=100;
disp("n = " + n);
A = gallery('poisson',n-2);
b = convn(([1,zeros(1,n-2),1]'|[1,zeros(1,n-1)]), 0.5*ones(3,3),'valid')';
x = reshape(A\b(:),n-2,n-2)';%

As before submit the job:

 >> j=c.batch('plot_demo', 'AutoAddClientPath', false);

To load 'x' into the local Matlab workspace:

 >> load(j,'x') 

Finally, plot the results:

 >> plot(x)