Thanks Patrick. Yep, I did see that but I couldn't parse it. I guess they are global variables but I'm not sure how to set them or use them. Sorry to be dense here...
On Wednesday, June 18, 2014 2:54:31 PM UTC-7, Patrick O'Leary wrote: > > 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 >>> >>>
