Hello Frank,
Unfortunately once you've already started up a Slurm job across 5 nodes, there's no current way to access all of those resources. Instead, we have a few options based on if MATLAB Parallel Server is installed on the cluster or not. You can run the ver command to list all of the products installed on the cluster.
If you do not see MATLAB Parallel Server listed in the ver output, you will only be able to use the resources of one node. You can call c=parcluster('local') and start as many workers in your pool as ther are cores on the machine. If you do see it listed, you can setup a Slurm cluster profile as was mentioned above.
Once you have a Slurm cluster profile setup, you can either submit a batch job like Edric mentioned above or use a Slurm batch script that calls some MATLAB code which opens up a parpool across multiple nodes.
Take the following code as an example. The first script starts up a session on one machine, and asks for just enough resources to launch the MATLAB client. Notice that we are only asking for one machine and one core. This is because the "my_parallel_code" file will be asking for the bulk of the resources in a seaparate call to the scheduler. Also note that the total time for this "outer" job to be long enough for the "inner" parallel job to also queue up and finish.
#!/bin/sh
#SBATCH -n 1 # 1 instance of MATLAB
#SBATCH --cpus-per-task=1 # 1 core per instance
#SBATCH --mem-per-cpu=4gb # 4 GB RAM per core
#SBATCH --time=2:30:00 # 2 hours, 20 minutes
# Add MATLAB to system path
# Modify as needed to load your MATLAB module/version
module load matlab/R2023b
# Run code
matlab -batch my_parallel_code
Here in "my_parallel_code", you can see that we first call the Slurm cluster profile we called earlier. Next, we call parpool with the total amount of workers that we want. This is going to submit a separate job request to Slurm and should be where you requrest the total bulk of your resources. Finally, we run our parallel code across the multiple workers/nodes that we requested.
function my_parallel_code
% Bring cluster profile into the workspace
c = parcluster('Slurm_profile_name_here');
% Specify total number of workers here. This can span multiple nodes
if isempty(gcp('nocreate')), c.parpool(50); end
% Actual parallel code here
parfor
....
end
% Function end
end
When you look at the squeue output, you should see two jobs -- the outer sbatch job that called MATLAB initially and is using one core and then the inner parpool job (with a name of Job#) that spans multiple nodes.
Let me know if you have any questions!
