Hi, I finally took the time to clean up my code to speed up prime-size FFT (which use a O(N^2) algo in both numpy and scipy). The code is there: https://github.com/cournape/numpy/tree/bluestein (most of the code is tests, because numpy.fft had almost none).
Bottom line: it is used only for prime numbers, and is faster than the current code for complex transforms > 500. Because of python + inherent bluestein overhead, this is mostly useful for "long" fft (where the speed up is significant - already 100x speed up for prime size ~ 50000). Several comments: - the overhead is pretty significant (on my machine, bluestein transfrom is slower for prime size < 500) - it could be used as such for real transforms, but the overhead would be even more significant (there is no bluestein transform for real transforms, so one needs to re-rexpress real transforms in term of complex ones, multiplying the overhead by 2x). There are several alternatives to make things faster (Rader-like transform, as used by fftw), but I think this would be quite hard to do in python without significant slowdown, because the code cannot be vectorized. - one could also decide to provide a chirp-z transform, of which Bluestein transform is a special case. Maybe this is more adapted to scipy ? - more generic code will require a few simple (but not trivial) arithmetic-like functions (find prime factors, find generator of Z/nZ groups with n prime, etc...). Where should I put those ? cheers, David _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
