Can you post the raw data? It seems like there are just a couple of "bad" sizes, I'd like to know more precisely what these are.
It's typical for FFT to perform better at a sample size that is a power of 2, and algorithms like FFTW take advantage of factoring the size, and "sizes that are products of small factors are transformed most efficiently." - Charles On Thu, Nov 14, 2013 at 10:18 AM, Max Linke <max_li...@gmx.de> wrote: > Hi > > I noticed some strange scaling behavior of the fft runtime. For most > array-sizes the fft will be calculated in a couple of seconds, even for > very large ones. But there are some array sizes in between where it will > take about ~20 min (e.g. 400000). This is really odd for me because an > array with 10 million entries is transformed in ~2s. Is this typical for > numpy? > > I attached a plot and an ipynb to reproduce and illustrate it. > > best Max > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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