Vectorizing/speeding up bsxfun multiplication-loop

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PetterS
PetterS on 12 Apr 2015
Commented: PetterS on 12 Apr 2015
I have a situation that looks like this:
C=zeros(1440,181,8);
for i=1:8
C(:,:,i)=sum(bsxfun(@times,A(:,:,31:end),reshape(B(:,i),1,1,[])),3);
end
Where A is of size=1440x181x2251 and B size=2221x8.
What I want to do is simply perform an element wise multiplication between A(:,:,31:end) and all the rows of the i’th column in B and then store the summation of the third dimension of this in C depending on the address of i. This seemingly simple command is destroying the runtime of my script and I can’t really figure out how to speed it up by avoiding the for loop. I tried replacing the for loop by a parfor to do several loops at the same time but I immediately run out of memory when I try that so it doesn’t work.
Any suggestions?
Thanks

Accepted Answer

Roger Stafford
Roger Stafford on 12 Apr 2015
Edited: Roger Stafford on 12 Apr 2015
You can try these to see which, if either, is faster than your current method:
C = reshape(reshape(A(:,:,31:end),[],2221)*B,1440,181,8);
or
C = reshape(reshape(A(1440*181*30+1:end),[],2221)*B,1440,181,8);
They should both give the same result as your code. The idea here is to convert to two-dimensional matrix multiplication and then reshape the result back to a three dimensional array, with the hope that Mathworks' matrix multiplication is more efficient than the combined action of 'sum' and 'bsxfun'.
  1 Comment
PetterS
PetterS on 12 Apr 2015
The first suggestion works like a dream! Thank you so much.
(the second one was also faster than my version but it caused a huge memory usage spike of about 10 Gb to run which I can’t afford)

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