Hey,
>Larger n yields greater efficiencies, right? Doing lots of small calls isn't >necessarilyas efficient as doing less large calls, so long as you can handle >the latency and the processor can stay fed with data. I figure >~4000 samples >is a good compromise, but maybe larger ones work out better, too. Keep in mind that the FFT has an algorithmic complexity of O(n*log n), thus smaller batches *should* in theory be easier on your CPU down to the point where call overheads become a problem. Keeping your FFT size near the capacity of your registers / L1 cache also can’t hurt ^^ Obviously you can still wait until you have accumulated a large batch of samples (a couple k sounds good?) and only then start running lots of small FFTs. If your samples are in a contiguous block of memory, your CPU should be able to prefetch those just fine. Ultimately you might just have to benchmark the two approaches and compare them - I’m not sure how well libraries like fftw deal with this usecase and what the call overhead is. Should fftw not work out, you could also try writing a small fixed-size fft yourself and inlining that, the code isn’t too bad (There are enough C-examples online). David Von: Discuss-gnuradio <discuss-gnuradio-bounces+dastw=gmx....@gnu.org> im Auftrag von Brian Padalino <bpadal...@gmail.com> Datum: Mittwoch, 17. Februar 2021 um 16:58 An: Marcus Müller <muel...@kit.edu> Cc: GNURadio Discussion List <discuss-gnuradio@gnu.org> Betreff: Re: Resampling radio data On Wed, Feb 17, 2021 at 10:05 AM Marcus Müller <muel...@kit.edu> wrote: Oh, sorry, didn't mean to imply that! FFT interpolation might work well (if you can live with the sinc sidelobes). If the bandwidth is already constrained to 20MHz/23MHz, then there would be no sidelobes - correct? I do have a question, though: Why do you go for a 4600-FFT? why not simply a 23 FFT? Larger n yields greater efficiencies, right? Doing lots of small calls isn't necessarily as efficient as doing less large calls, so long as you can handle the latency and the processor can stay fed with data. I figure ~4000 samples is a good compromise, but maybe larger ones work out better, too. For reference, here's some performance benchmarks for FFTW (randomly chosen processor): http://www.fftw.org/speed/Pentium4-3.06GHz/ Single precision, non powers of 2, 1d transforms are some good benchmarks to look at for something like this. Obviously some numbers are better than others, but the general trend seems to skew towards the mid-thousands as being a decent number to target for maximizing throughput. Brian
