parallelization and mex-compiled code
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I want to optimize a mex-compiled function (fortran-90 source) defined over an 1D interval by computing its values on a sufficiently fine sampling. It works fine with a for-loop but when I try parfor (for speed) I get crashes in the mex-compiled code (getting a error from one of the workers). Is this a documented problem, and does anyone have suggestions how to localize what goes wrong?
I run MatlabR2013a and Ubuntu 13.10 on a 16 core (32 virtual) machine and I get 12 workers when I do matlabpool.
1 Comment
Matt J
on 6 Feb 2014
No, there is no general prohibition against using mex files with parfor. Show us the plain for-loop and the parfor version.
Accepted Answer
Matt J
on 6 Feb 2014
Edited: Matt J
on 6 Feb 2014
You should try running a plain for-loop first, but with the iterations in random order, i.e., instead of
for i=1:n
...
end
run as
for i=randperm(n)
...
end
This is a good way to test whether your code is independent of the order of the iterations (a basic requirement of parfor) before the Parallel Computing Toolbox even gets involved.
5 Comments
Matt J
on 6 Feb 2014
Can you try it on a different machine to see if it's hardware problem? I don't see anything wrong with the code.
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