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How to set up a Matlab parallel cluster for thread-based environment

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Maria
Maria on 16 Jun 2021
Commented: Raymond Norris on 16 Jun 2021
Hi,
I am starting to explore Matlab Parallel functionalities, and, I have to say, I am a bit confused about the process-based vs. thread-based environment.
First question: I have 2 clusters, namely, the local cluster and the "MatlabCluster" (remote cluster with 8 nodes, 32 workers). If I use
poop = parpool('MatlabCluster');
the default environment is the "process-based" environment. Correct? Can I use the remote cluster in a "thread-based" environment? If I do
pool = parpool('thread');
only the local cluster switches to 'thread'. Can I do the same with the remote cluster?
Second question: I am experimenting with distributed arrays. However, if I start the 'MatlabCluster' (remote cluster), I get few errors and the last error message is
No workers are available for FevalQueue execution
This happens for the line of code that uses distrubuted arrays. I read that FevalQueue is not supported in "thread-based environment". Does this error mean that, by default, the remote cluster is starting as "thread-based"? (which would contradict my first hypotesis?).

Accepted Answer

Raymond Norris
Raymond Norris on 16 Jun 2021
The thread-based pool only runs on the same machine as the MATLAB client, similar to a local process-based pool. However, unlike the local pool, the threaded pool has a fixed startup size, which is the value returned by maxNumCompThreads. If you wanted a different number of workers started with a threads pool, you have to set it first in maxNumCompThreads. For example:
% Let's assume you have 8 physical cores, but only want to start a threaded
% pool of 2 workers.
old_threads = maxNumCompThreads(2);
parpool("threads");
Starting parallel pool (parpool) ... Connected to the parallel pool (number of workers: 2).
ans =
ThreadPool with properties: NumWorkers: 2
Keep in mind that setting maxNumCompThreads, in addition to effecting the number of workers started, may have an effect on your other MATLAB code.
You'll need to post a bit more (code, errors) to decipher the FevalQueue error.
  2 Comments
Raymond Norris
Raymond Norris on 16 Jun 2021
To get the memory on the Linux nodes, run
free -mth
This will give you the free & used memory.

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