Effacer les filtres
Effacer les filtres

IFFT slow down with using gpuArray

16 vues (au cours des 30 derniers jours)
Michael
Michael le 3 Mai 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.
  4 commentaires
James Lebak
James Lebak le 3 Mai 2013
Modifié(e) : James Lebak le 3 Mai 2013
When I time this on MATLAB R2013a, 3.5 GHz Xeon, with a Tesla C2075 GPU, I see 0.36 s for the IFFT of a 32768x1024 matrix on the CPU and 0.051s on the GPU. Here is the code I used:
x=gpuArray.ones(32768,1024);
gd=gpuDevice;
tic;y=ifft(x);wait(gd);toc
xc=gather(x);
tic;y=ifft(xc);toc
And the output:
Elapsed time is 0.050705 seconds.
Elapsed time is 0.364836 seconds.
I would be interested to know what this code shows you, and also whether having the other array that you mentioned in memory changes the performance. I didn't see a change, but I don't have access to this specific card that you have.
Michael
Michael le 3 Mai 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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Réponse acceptée

Matt J
Matt J le 3 Mai 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.
  10 commentaires
Matt J
Matt J le 3 Mai 2013
Also, Matt J, my apologizes about the program. The code I attached is part of a project. Is there a way to attach .m files in this forum?
Just give values for
NumberofAlines = pdHeader(1);
nAlineLength = pdHeader(2);
nPaddingFactor = pdInit(4);
I assume that pdBuffer is the 32768x1024 array?
Michael
Michael le 3 Mai 2013
Of course. Thank you:
NumberofAlines = 1024
nAlineLength = 4096
nPaddingFactor = 8

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Plus de réponses (1)

James Lebak
James Lebak le 3 Mai 2013
Modifié(e) : James Lebak le 4 Mai 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.
  5 commentaires
Michael
Michael le 5 Mai 2013
James Lebak you were correct. Single-precision is performing significantly more efficient than the double-precision data. Matt J and James Lebak thank you for all your help.
Matt J
Matt J le 6 Mai 2013
Modifié(e) : Matt J le 6 Mai 2013
If James was right, then why didn't you accept his Answer instead of mine???

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