Better parallelization than parfor?

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schlapp
schlapp 2020년 1월 27일
편집: Matt J 2020년 1월 28일
Hello,
I have a function fun(vec(1:n),Nmax) with two outputs: (i) a matrix g(1:n,1:Nmax) and (ii) a vector tau(1:Nmax), as Nmax eigenfunctions and eigenvalues of a matrix constructed within fun. Now I want to get this output for all values withing a vector vec2(1:n). The simplest way is a for-loop
for i=1:n
[g(1:n,1:Nmax,1:n),tau(1:n,1:Nmax)] = fun(vec(1:n),Nmax,vec2(i))
end
This is however slow. Replacing it with a parfor-loop
parfor i=1:n
[g(:,:,i),tau(i,:)] = fun(vec(:),Nmax,vec2(i))
end
is quite a bit faster. I was wondering if there is a way to accelerate this even further by threading over the vector vec2? Somehow I cannot find the correct way without rewriting the function fun in several ways. Maybe the problem is that g is already a matrix, therefore fun(vec,Nmax,vec2') does not evaluate things in the correct dimension?
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Matt J
Matt J 2020년 1월 28일
If i write sin(vec+vec'), the output is a nxn matrix, where n is the length of vec. However within this notation I am limited to automatic threading over only two dimensions.
The extension would be
sin(vec+vec.'+ reshape(vec,1,1,[]));

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Matt J
Matt J 2020년 1월 27일
The reason why I think it could be improved is that MATLAB tells me that vec in my above example is a 'broadcast' variable and might slow things down.
But you said vec2 was the problem, not vec.
In any case, if each call to fun requires all of vec then you have no choice but to broadcast it.
  댓글 수: 2
Edric Ellis
Edric Ellis 2020년 1월 28일
As @Matt J pointed out, if each iteration needs all of vec, then there's not much you can do. That warning is shown for all broadcast variables, regardless of whether or not they actually cause a performance degradation.
You can use ticBytes and tocBytes to see how much data is actually being transmitted to/from your parfor loop. If vec is a numeric vector of length n, then my suspicion would be that it probably has no discernable affect on the loop time.

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