MATLAB: IFFT slow down with using gpuArray

gpuifft

Two sets of data A (4096 x 1024) matrix and B (32768 x 1024) matrix have been transferred to the GPU using gpuArray. A is passed into the FFT function and has shown a significant speed increase in comparison to the CPU A data. B is passed into the IFFT function and has shown approximately a 50% decrease in efficiency in comparison to the CPU B data. Is there a reason why the IFFT function does not have the speed increase proportional to the FFT function? I understand the sizes differ but I do no understand why the GPU implemented IFFT is slower then the CPU implemented IFFT. Also, the tic toc function and the run and time function were used to time the results. Thank you for your help.

Best Answer

What graphics card do you have? How much RAM does it have? It could be that the larger array is just having a harder time because of memory constraints.

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MATLAB: CPU vs GPU – Is it reasonable

While I can't comment exactly on the CPU/GPU comparison for your specific setup, I can say that the general rule of thumb in GPU computing is that operating on as much data as possible in a single call by vectorizing your code will provide the best performance. With that said, since you are already using the bsxfun function I would go a step further and use the following code to avoid the for loop and operate on all of your data in a single call.

Also, the timeit/gputimeit functions are the best choice for comparing CPU and GPU execution as they each provide an average time over multiple runs. Furthermore, gputimeit takes into account the fact that GPU operations perform asynchronously, while tic/toc does not.

x1GPU=gpuArray.randn(3,1,10000);
x2GPU=gpuArray.randn(3,5000);
gputimeit(@()bsxfun(@minus, x1GPU, x2GPU))
x1CPU=randn(3,1,10000);
x2CPU=randn(3,5000);
timeit(@()bsxfun(@minus, x1CPU, x2CPU))

I can confirm that when I run the code you provided, it takes about 4 seconds on the GPU and 1.5 seconds on the CPU. For the code that I have provided, gputimeit reports an average time of 0.0855 seconds on the GPU while timeit reports (again) an average of 1.5 seconds on the CPU.

The bottom line is that CPU for loops combined with GPU computing in the loop body generally does not provide the best performance. You should always try to replace code like this with a vectorized version if possible.

MATLAB: Zero padding or not

   fft(x,1024)

Will automatically zero pad your signal to 1024 points and perform FFT

Best Answer

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MATLAB: CPU vs GPU – Is it reasonable

MATLAB: Zero padding or not

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