Why is GPU Array slow for matrix multiplication
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when I run the following code:
TRIALS=1;
t = tic;
for i = 1:TRIALS
A = rand(1280,1280,'single','gpuArray');
for iter = 1:100
A = A*A;
end
end
ITERATION_TIME = toc(t)
it only takes 0.0039 seconds consistently. But when I set
TRIALS=5
It takes 4.9892 seconds, a 1000x performance hit for only 5 times the number of iterations.
I realize that gpuTimeit() can be used to time code on the GPU specifically but for the overall program to complete, why doesn't the time scale with the number of trials?
I am running this on a Mac Pro 2014 on an NVIDIA GeForce GT 750M 2048 MB, MATLAB R2016b.
1 comentario
Joss Knight
el 24 de Abr. de 2017
Your first number is wrong, because the computation hadn't actually finished when you called toc. You need to read the doc page on measuring performance on the GPU.
Respuestas (2)
Swathik Kurella Janardhan
el 21 de Abr. de 2017
I tried your code on a Windows 10, MATLAB R2016b and only a few attempts I see the 1000x performance hit for 5 Trials and in other attempts I see the execution time as expected it increased only by number of trials.
There are multiple factors which determine a GPU's performance. To measure the GPU performance you can run the below benchmark tests:
>>openExample('distcomp/paralleldemo_gpu_benchmark')
>>paralleldemo_gpu_benchmark
Built in example, will give information on PCI bus speed (send and gather speed), GPU memory read/write and peak calculation performances for double precision matrix multiply.
>>gpuBench
Written by MathWorks Parallel Computing Team and available on file exchange. http://www.mathworks.com/matlabcentral/fileexchange/34080-gpubench
Main advantage over the example script is this will do a variety of tests involving both memory and compute intensive tasks in both single and double precision. It will also offer comparison between a relatively normal display card and a reasonable compute card. The performance of these are matched with the version of Matlab run. (gpuBench will soon be updated to cover the missing releases to R2016b).
Jeffrey Daniels
el 30 de En. de 2018
@BMWv It really depends on your application. Assuming one trial is not dependent on a previous trial and if you know the whole matrix before you start your iter loop, you could use pagefun and not use the TRIALS loop at all. Your matrix would need to start with size (1280,1280,TRIALS). This should be much faster for large TRIALS. See pagefun documentation for more examples.
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