How do MATLAB workers/PC cores divide the work in a parallelized optimization inside a parfor loop?

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For example:
parfor i = 1:20
options = optimoptions('ga','UseParallel',true,'UseVectorized',false);
x = gamultiobj(ObjectiveFunction,[],options);
Let's say there are 10 workers, and we are running the optimization algorithm with parallelization within a parallelized parfor loop.
Although my sample is not large, I have noticed that this is faster than using a simple for loop.
According to MATLAB docs, a parfor inside a parfor does not work. Yet this combination (which in the end is a parfor inside a parfor, I guess) does work.
Thus, my questions are:
  • How do workers divide the work? I notice that the first 10 loop cases are started at the same time, but do the workers then stop their loop iteration and help out whichever started the gamultiobj first?
  • Assuming "n" workers, do the parfor and for approaches deliver the same performance when num of cases >> n?

Accepted Answer

Raymond Norris
Raymond Norris on 21 Aug 2020
Hi Taro,
Do you want to run gamultiobj 20 (in this example) times? If so, then parfor will run quicker than for. However, the parellel for loop you've written will negate the parallel for loop that is running in the optimization algorthim.
I suspect you simple need to call
options = optimoptions('ga','UseParallel',true,'UseVectorized',false);
x = gamultiobj(ObjectiveFunction,[],options);
And let the inner parallel for loop do its work.
Mitsu on 22 Aug 2020
Got it. I take that as a general rule, if the outer loop has more cases than number of workers, it makes sense to use an outer parfor loop; otherwise keep only the inner parfor (inside the optimization).

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