是否值得将FFT计算卸载到嵌入式GPU？ [英] Is it worth offloading FFT computation to an embedded GPU?
问题描述
我们正在考虑从专用数字信号处理芯片移植应用程序，以在通用x86硬件上运行。该应用程序进行大量的傅里叶变换，从简要的研究看，FFT似乎非常适合GPU而不是CPU的计算。例如，本页面具有Core 2 Quad和GF 8800 GTX的基准测试，在使用GPU时，计算时间缩短了10倍。
http://www.cv.nrao.edu/~pdemores/gpu/
将GPU计算卸载到GPU上，只能在适当的PCIe总线上使用肉色的显卡，甚至嵌入式GPU能提高性能？
在x86硬件和GPU上开发了FFT例程（在CUDA之前， 7800 GTX硬件）从我自己的结果发现，使用较小的FFT（低于2 ^ 13），CPU更快。在这些尺寸之上，GPU更快。例如，2×16大小的FFT在GPU上比CPU上的等效变换更快地计算出2-4倍。看到下面的时间表（所有时间都是在几秒钟内，比较一个3GHz奔腾4与7800GTX，这项工作是在2005年完成的，旧的硬件和正如我所说，非CUDA。较新的图书馆可能会显示更大的改进）
N FFTw（s）GPUFFT GPUFFT MFLOPS GPUFFT加速
8 0 0.00006 3.352705 0.006881
16 0.000001 0.000065 7.882117 0.010217
32 0.000001 0.000075 17.10887 0.014695
64 0.000002 0.000085 36.080118 0.026744
128 0.000004 0.000093 76.724324 0.040122
256 0.000007 0.000107 153.739856 0.066754
512 0.000015 0.000115 320.200892 0.134614
1024 0.000034 0.000125 657.735381 0.270512
2048 0.000076 0.000156 1155.151507 0.484331
4096 0.000173 0.000215 1834.212989 0.804558
8192 0.000483 0.00032 2664.042421 1.510011
16384 0。 001363 0.000605 3035.4551 2.255411
32768 0.003168 0.00114 3450.455808 2.780041
65536 0.008694 0.002464 3404.628083 3.528726
131072 0.015363 0.005027 3545.850483 3.05604
262144 0.033223 0.012513 3016.885246 2.655183
524288 0.072918 0.025879 3079.443664 2.817667
1048576 0.173043 0.076537 2192.056517 2.260904
2097152 0.331553 0.157427 2238.01491 2.106081
4194304 0.801544 0.430518 1715.573229 1.861814
通过其他海报将数据传输到/从GPU是你的命中。可以在CPU上执行更小的FFT，一些实现/大小完全在缓存中。这使得CPU成为小型FFT的最佳选择（低于〜1024点）。另一方面，如果需要对数据进行大批量的工作，那么GPU的最小移动速度就会降低，GPU会击败CPU。
如果您想要快速执行FFT，建议使用FFTW，或者如果您想要更快（商业））实施，则建议使用FFTW。对于FFTW，使用FFTW_Measure标志执行计划将测量和测试特定硬件的最快速的FFT例程。我在这个问题。
对于GPU实现，您无法比NVIDIA CUDA提供的更好。自从我在7800GTX上进行了实验后，GPU的性能显着提高，所以建议给出SDK的特定要求。
We are considering porting an application from a dedicated digital signal processing chip to run on generic x86 hardware. The application does a lot of Fourier transforms, and from brief research, it appears that FFTs are fairly well suited to computation on a GPU rather than a CPU. For example, this page has some benchmarks with a Core 2 Quad and a GF 8800 GTX that show a 10-fold decrease in calculation time when using the GPU:
http://www.cv.nrao.edu/~pdemores/gpu/
However, in our product, size constraints restrict us to small form factors such as PC104 or Mini-ITX, and thus to rather limited embedded GPUs.
Is offloading computation to the GPU something that is only worth doing with meaty graphics cards on a proper PCIe bus, or would even embedded GPUs offer performance improvements?
Having developed FFT routines both on x86 hardware and GPUs (prior to CUDA, 7800 GTX Hardware) I found from my own results that with smaller sizes of FFT (below 2^13) that the CPU was faster. Above these sizes the GPU was faster. For instance, a 2^16 sized FFT computed an 2-4x more quickly on the GPU than the equivalent transform on the CPU. See a table of times below (All times are in seconds, comparing a 3GHz Pentium 4 vs. 7800GTX. This work was done back in 2005 so old hardware and as I said, non CUDA. Newer libraries may show larger improvements)
N FFTw (s) GPUFFT (s) GPUFFT MFLOPS GPUFFT Speedup 8 0 0.00006 3.352705 0.006881 16 0.000001 0.000065 7.882117 0.010217 32 0.000001 0.000075 17.10887 0.014695 64 0.000002 0.000085 36.080118 0.026744 128 0.000004 0.000093 76.724324 0.040122 256 0.000007 0.000107 153.739856 0.066754 512 0.000015 0.000115 320.200892 0.134614 1024 0.000034 0.000125 657.735381 0.270512 2048 0.000076 0.000156 1155.151507 0.484331 4096 0.000173 0.000215 1834.212989 0.804558 8192 0.000483 0.00032 2664.042421 1.510011 16384 0.001363 0.000605 3035.4551 2.255411 32768 0.003168 0.00114 3450.455808 2.780041 65536 0.008694 0.002464 3404.628083 3.528726 131072 0.015363 0.005027 3545.850483 3.05604 262144 0.033223 0.012513 3016.885246 2.655183 524288 0.072918 0.025879 3079.443664 2.817667 1048576 0.173043 0.076537 2192.056517 2.260904 2097152 0.331553 0.157427 2238.01491 2.106081 4194304 0.801544 0.430518 1715.573229 1.861814
As suggested by other posters the transfer of data to/from the GPU is the hit you take. Smaller FFTs can be performed on the CPU, some implementations/sizes entirely in the cache. This makes the CPU the best choice for small FFTs (below ~1024 points). If on the other hand you need to perform large batches of work on data with minimal moves to/from the GPU then the GPU will beat the CPU hands down.
I would suggest using FFTW if you want a fast FFT implementation, or the Intel Math Library if you want an even faster (commercial) implementation. For FFTW, performing plans using the FFTW_Measure flag will measure and test the fastest possible FFT routine for your specific hardware. I go into detail about this in this question.
For GPU implementations you can't get better than the one provided by NVidia CUDA. The performance of GPUs has increased significantly since I did my experiments on a 7800GTX so I would suggest giving their SDK a go for your specific requirement.
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