Difference between simple matlabpool parfor and batch parfor.
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I see a huge difference between apparently similar approaches and would like to understand what's happening and why the discrepancy.
I just created this M-file
% code
mywave.m
parfor i=1:10024
A(i) = sin(i*2*pi/1024);
end
And tried to run it using 2 different approaches:
%code
matlabpool open 100
%connected to 100 labs.
tic
mywave
toc
Elapsed time is 0.652927 seconds.
%code
2. batch('mywave','matlabpool',100)
MaximumNumberOfWorkers : 101 MinimumNumberOfWorkers : 101 Elapsed time is 166.722475 seconds
My configuration is with 132 processors (workers) passing all the validatiions. connected to 100 labs.
With resource list parameter as : -l walltime=12:00:00 -l nodes=11:ppn=12
(i have a linux cluster with 12 nodes & 12 ppn , with shared file system)
Thanks in advance.
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Edric Ellis
le 31 Juil 2012
The amount of computation that your PARFOR loop contains is far too small to benefit from launching such a large matlabpool. Launching 100 workers and having them all set up communications etc. takes a long time - much longer than it takes for MATLAB to run the body of your FOR loop. You can only expect PARFOR to speed up your program when the amount of work to be done exceeds the overheads of setting up the workers and launching the parallel loop.
2 commentaires
Walter Roberson
le 31 Juil 2012
See http://www.mathworks.co.uk/matlabcentral/answers/44734-there-is-aproblem-in-parfor for a small mathematical analysis of why parfor can be slower.
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