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:
[Discuss-gnuradio] Re: GNU Radio GPGPU WIP Branch Status?
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:
Re: [Discuss-gnuradio] GPU progress?
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!!