The flags are defined in the FFTW documentation: http://www.fftw.org/doc/Planner-Flags.html
On Wednesday, June 18, 2014 4:30:30 PM UTC-5, Ethan Anderes wrote: > > Hi Tim: > > Do you happen to know where I can find some documentation or code snippets > on using FFTW.MEASURE or FFTW.PATIENT? I also have some performance > critical code that I've failed to speed up with plan_fft and plan_ifft. > After reading your suggesting I tried to search for info on FFTW.MEASURE or > FFTW.PATIENT but couldn't really find anything (I can't follow the source > code either). > > Any help would be greatly appreciated. > > Cheers, > Ethan > > On Wednesday, June 18, 2014 4:40:10 AM UTC-7, Tim Holy wrote: >> >> Have you profiled it? Is most of the time spent in the ffts? Assuming it >> is the >> ffts that dominate, try preplanning with FFTW.MEASURE or FFTW.PATIENT. >> >> I seem to recall that some months ago I posted some Matlab/Julia FFT >> benchmarks to one of the mailing lists or the github repository. I >> suspect >> that Matlab has some clever tricks up their sleeve for >> fft-planning---they seem >> to be able to settle on a good algorithm with much less time than I find >> I need >> with FFTW.MEASURE or FFTW.PATIENT. It would be lovely to figure out a >> better >> approach; with 3d FFTs I often find that to get good performance I need >> to >> devote about 60s to planning for each different size I run, which is a >> bit of a >> pain. >> >> --Tim >> >> On Wednesday, June 18, 2014 03:35:06 AM Oliver Lylloff wrote: >> > Hello all, >> > >> > I'm computing a 2D convolution with fft and seeing a timing difference >> > between Matlab and Julia, with Matlab being about 2x faster than Julia. >> > I've simplified my code down to an example (see gist here: >> > https://gist.github.com/1oly/51f39a831cef3931e7b8). The bottleneck of >> my >> > code is evaluating the function >> > >> > function func(b,x,Fps) >> > r = fftshift(ifft(fft(x).*Fps)) - b; >> > return r >> > end >> > >> > Timings are after one initial run to remove overhead and Matlab is >> single >> > thread. >> > >> > Matlab (-singleCompThread) = 0.0026s >> > Julia = 0.005s >> > >> > Does other people see this difference as well? Any suggestions to >> possible >> > speed-ups? >> > >> > Best, >> > Oliver >> >>
