[Numpy-discussion] strange runtimes of numpy fft

[Charles Waldman]

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_linke at 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
>
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