Hi Has anyone able to successfully improve CUDA-Enabled GNURadio's performance? At the moment I am very new at this stuff so I am just looking at Martin's code without any really solid understanding. I know that the gr_benchmark10_test.py performance is slow computing on GPU due to the over-head memory calls to and from the CPU and GPU, and that if more compution/work is done per call, the GPU can out-perform the CPU. However looking at the gr_benchmark10 code, it seems that very trivial computations are being done to compare the CPU and GPU. Specifically:
testblock3= cuda.fir_filter_fff(1,taps) > testblock4= cuda.multiply_const_ff(1.0) > testblock5= cuda.multiply_const_ff(1.0) > testblock6= cuda.multiply_const_ff(1.0) I attempted to "increase" the GPU performance by inserting very large floating point numbers as parameters to cuda.multiply_const_ff and also messing around taps which is declared by: taps=range(1,64,1) But in doing so, I assume that I am passing in "more work" to be done so the GPU should be faster, but it is not. the CPU still takes fractions of a second to complete (with large floating points) while the GPU takes a little over 1 second. - Following this thread: http://lists.gnu.org/archive/html/discuss-gnuradio/2009-01/msg00378.html I would like to approach the problem by increasing computation intensity, thats why I am changing the benchmark parameters, but it doesnt seem to work, Am I approaching this correctly? - From this thread: http://lists.gnu.org/archive/html/discuss-gnuradio/2008-11/msg00292.html > If I benchmark a single block with a big output_multiple then I do see > performance increases. > > How do I do the above? How have the experts (Martin, Achilleas) been able to tweak the performance of CUDA-Enabled GNURadio to show that GPU computing can indeed be faster? - Is there anyway to measure the time the memory calls to and from CPU and CUDA? This way we can know what exactly is the overhead. Please help!!
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