GPU knnsearch performs slower than CPU for large matrices?
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I am currently running a knnsearch on the CPU, with a large number of query points (10 million by 3), where I want the index of where each row of the 10 million belongs with respect to a matrix of 2,500 by 3.
Running knnsearch, without the GPU, takes roughly 11 seconds, but with the GPU it takes about 45 seconds. I did notice that lowering the query points to 1 million, led to the GPU taking roughly 1.5 seconds and the CPU now only takes 1.3 seconds.
Ultimately I thought using the GPU would do the knnsearch faster, but it doesn't seem to be the case. Am I implementing this correctly? Any other advice on how to get indeces from knnsearch faster, either with GPU or without is greatly appreciated!
Example code below:
Note: I will mention that my "X" and "Y" for my implementation are not generated by randn, but are the same dimensions. I don't think that would change the interpretation of what I am trying to do, but just thought I'd mention it.
rng default
%%% 10 million queries
%%Using CPU
X=randn(2500,3);
Y=randn(10000000,3);
c=@() knnsearch(X,Y);
tcpu=timeit(c);
%%Using GPU
gx=gpuArray(X);
gy=gpuArray(Y);
g=@() knnsearch(gx,gy);
tgpu=gputimeit(g);
%%% Now just 1 million queries
%%Using CPU
X=randn(2500,3);
Y=randn(1000000,3);
c2=@() knnsearch(X,Y);
tcpu2=timeit(c2);
%%Using GPU
gx=gpuArray(X);
gy=gpuArray(Y);
g2=@() knnsearch(gx,gy);
tgpu2=gputimeit(g2);
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Réponses (1)
Damian Pietrus
le 19 Mar 2024
Hello Ishan,
There is some overhead when moving variables between the CPU and the GPU. For shorter running calculations, this data overhead can account for a decent percentage of your overall compute time. As a comparison, try measuring both the overall GPU time and the time just for the GPU computation. This will give you an idea of how much of the total time is spent just on data transfer overhead.
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