Same gpu operation in loop but two speeds
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I am running the code below on Windows 10 64 bits with an intel i5-8300H and a Nvidia GTX 1060:
dev = gpuDevice;
time = zeros(1, length(t)); % preallocation of time
time2 = zeros(1,length(t)); % preallocation of time2
for i = 2:length(t)-1 %looping over time vector
% Computation of dot product between A(:, i:-1:1) and C(:,1:i,1) + C(:,1:i,2) for each row
approx_conv_pair = sum(reshape(A(txN-i*N+1:txN-N).*(C(1:(i-1)*N) + C(txN+1:txN+(i-1)*N)),[N,i-1]),2);
% Computation of dot product between B(:, i:-1:1) and C(:,1:i,1) - C(:,1:i,2) for each row
approx_conv_impair = sum(reshape(B(txN-i*N+1:txN-N).*(C(1:(i-1)*N) - C(txN+1:txN+(i-1)*N)),[N,i-1]),2);
A, B and C are gpuArrays of size, respectively, (N, length(t)) (N, length(t)) and (N, length(t), 2) .
My indexing simply accesses A(:, i:-1:2), B(:, i:-1:2), C(:, 1:i-1, 1) and C(:, 1:i-1, 2) as column vectors.
Here's what I obtain when I plot time and time2 with respect to looping iterator i when N=252 and length(t) = 13200:
Does anybody knows why there's such a difference between execution times?
Is it due to my way of coding or something linked to overhead time on the GPU?
FYI I tried to invert the order of approx_conv_pair and approx_conv_impair and I observe the same problem (second operation almost twice as fast).
Thank you in advance!
Joss Knight 2020 年 5 月 6 日
MATLAB will do some optimisations when it sees you are doing the same thing repeatedly. In your case, the optimisation that applies is that memory needs to be allocated for the temporary variables you are creating such as the result of A(txN-i*N+1:txN-N).*(C(1:(i-1)*N). Since this changes in size each iteration new allocations keep having to be done; but in the second operation, the memory allocated in the first operation has been pooled and can be reused. The cost of allocating memory (except for the output) is eliminated and you just see the raw cost of computation.