On Tue, 26 Nov 2013 01:02:40 -0800
"Dinesh Vadhia" <[email protected]> wrote:
> Probably a loaded question but is there a significant performance difference
> between using MKL (or OpenBLAS) on multi-core cpu's and cuBLAS on gpu's.
> Does anyone have recent experience or link to an independent benchmark?
>
Using Numpy (Xeon 5520 2.2GHz):
In [1]: import numpy
In [2]: shape = (450,450,450)
In [3]: start=numpy.random.random(shape).astype("complex128")
In [4]: %timeit result = numpy.fft.fftn(start)
1 loops, best of 3: 10.2 s per loop
Using FFTw (8 threads (2x quad cores):
In [5]: import fftw3
In [7]: result = numpy.empty_like(start)
In [8]: fft = fftw3.Plan(start, result, direction='forward', flags=['measure'],
nthreads=8)
In [9]: %timeit fft()
1 loops, best of 3: 887 ms per loop
Using CuFFT (GeForce Titan):
1) with 2 transfers:
In [10]: import pycuda,pycuda.gpuarray as gpuarray,scikits.cuda.fft as
cu_fft,pycuda.autoinit
In [11]: cuplan = cu_fft.Plan(start.shape, numpy.complex128, numpy.complex128)
In [12]: d_result = gpuarray.empty(start.shape, start.dtype)
In [13]: d_start = gpuarray.empty(start.shape, start.dtype)
In [14]: def cuda_fft(start):
....: d_start.set(start)
....: cu_fft.fft(d_start, d_result, cuplan)
....: return d_result.get()
....:
In [15]: %timeit cuda_fft(start)
1 loops, best of 3: 1.7 s per loop
2) with 1 transfert:
In [18]: def cuda_fft_2():
cu_fft.fft(d_start, d_result, cuplan)
return d_result.get()
....:
In [20]: %timeit cuda_fft_2()
1 loops, best of 3: 1.05 s per loop
3) Without transfer:
In [22]: def cuda_fft_3():
cu_fft.fft(d_start, d_result, cuplan)
pycuda.autoinit.context.synchronize()
....:
In [23]: %timeit cuda_fft_3()
1 loops, best of 3: 202 ms per loop
Conclusion:
A Geforce Titan (1000€) can be 4x faster than a couple of Xeon 5520 (2x 250€)
if your data are already on the GPU.
Nota: Plan calculation are much faster on GPU then on CPU.
--
Jérôme Kieffer
tel +33 476 882 445
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