IFFT slow down with using gpuArray

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Michael on 3 May 2013
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.
Michael on 3 May 2013
Thank you for the test case. When I run this same program the output is:
Elapsed time is 0.466822 seconds.
Elapsed time is 0.863542 seconds.
I believe the Tesla C2075 has a faster processing time than the GeForce GT 630M. However, your efficiency is terrific with a speed up of approximately 600% and mine was 85%. Why would there be such a difference? Thank you

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Accepted Answer

Matt J
Matt J on 3 May 2013
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.
Michael on 3 May 2013
Of course. Thank you:
NumberofAlines = 1024
nAlineLength = 4096
nPaddingFactor = 8

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More Answers (1)

James Lebak
James Lebak on 3 May 2013
Edited: James Lebak on 4 May 2013
The GeForce GT630M is a mobile graphics card. Frequently, these cards don't perform as well in double-precision as they do in single-precision. If your application can handle single-precision, you can try the IFFT in single and see if that gives you better performance. If you need double precision performance, you might want to try a different card.
This especially applies if the card in question is compute capability 3.0. You can find out the compute capability of the card in MATLAB from the structure returned by 'gpuDevice'.
Edit: removed incorrect identification of the 630M.
Matt J
Matt J on 6 May 2013
Edited: Matt J on 6 May 2013
If James was right, then why didn't you accept his Answer instead of mine???

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