I just finished reworking some code I wrote a while ago that drives FFTW in parallel for repeated transforms. My original code only operated in a restrictive manner, but I finally got around to letting it run in arbitrarily strided arrays. The idea is to plan an FFT of a certain structure on one thread, and then split the data up and apply the plan on multiple threads. (Since I work with images and like my arrays to be centered at N/2, my implementation has the threads do some change of variables magic with hairy strided pointer walking--more memory efficient than fftshift.) I'm wondering if people think this could be useful, and if it would be a small effort to bring it to scipy? here's a look at the c++ code and the weave wrapping https://cirl.berkeley.edu/trac/browser/bic/branches/rtools_weave/root/recon/... https://cirl.berkeley.edu/trac/browser/bic/branches/rtools_weave/root/recon/... The code is definitely working and passes comparison tests within reasonable accuracy. That said, it's sure to be rough around the edges (I'm not terribly careful coding in C++). Has: *If an N-D array is to be split up into T transforms (of any rank R <= N), and T can be split evenly into P threads, then the tool parallelizes the fft calls *inplace and out of place xforms *shifted or non-shifted xforms Hasn't: *no Microsoft threads (no clue how they work) *no real-to-complex or c2r transforms *no fftw plan caching -- other than what the fftw libs keep persistent *may not work for negatively strided arrays.. haven't checked *definitely won't work for funky strides (==0) of course