Hi Bogdan Yes, I understand that you were using relative error as the metric of comparison. My point was that the pointwise absolute deviation from numpy's values were diverging quickly and when I run tests on my data ( I am working on convolutions of voxelized solid models) using numpy's fft I get exactly the results I am looking for, with single precision. Therefore while the relative error remains small, and justifiably so, the absolute deviation tends to be the important metric for my application, i.e. comparing two shapes created via convolutions. So I guess I will try using double precision numbers, or stick to smaller arrays. Thank you for your help, and for pyfft.
Cheers, Saigopal On Tue, Jan 18, 2011 at 12:39 AM, Bogdan Opanchuk <manti...@gmail.com>wrote: > Hi Saigopal, > > On Tue, Jan 18, 2011 at 5:14 PM, Saigopal Nelaturi <saigo...@gmail.com> > wrote: > > Thanks for the quick response. My operating specs are exactly the same as > > yours, and when I run your test I get an error of ~3e-7. But I think that > > number may have to do with dividing by the norm of the convolution in the > > expression in the last line of your test > > Of course, it is the relative error. Absolute error would depend on > the size of the array and hence would not provide any information. > Relative error of the order of 1e-6 - 1e-7 when working with > single-precision numbers is normal. > > > If the GPU convolution norm is high and the difference between cpu and > gpu > > values of convolution is relatively low, you would get a low value for > the > > division. > > Exactly. That's what I am checking in my code. You can try comparing > numpy.fft.fftn() results for single- and double- precision numbers - > you will get the same relative error. > > > The ratio of those two numbers is ~3e-7. But the norm of the difference > between the two convolutions (cpu vs gpu) is high (69734). Is there > something I am missing? > > It is high because the array size is extremely large. Probably, the > following last line would illustrate my point better: > > print numpy.max(numpy.abs((corr_cpu - corr_gpu) / corr_cpu)) > > This gives 6.6e-7 on my machine. This means that the relative > difference between every pair of elements with the same index in > CPU-produced array and GPU-produced array is smaller than 6.6e-7. You > cannot really ask for much more when you are using single-precision > numbers — it is defined by the size of the mantissa. > > So, if this small difference is the actual reason of you getting > "garbage data", the only solution is to start using double precision > numbers (or maybe review your algorithms). > > Best regards, > Bogdan >
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