Numpy fft.pack vs FFTW vs Implement DFT on your own

问题

I am currently need to run FFT on 1024 sample points signal. So far I have implementing my own DFT algorithm in python, but it is very slow. If I use the NUMPY fftpack, or even move to C++ and use FFTW, do you guys think it would be better?

回答1:

If you are implementing the DFFT entirely within Python, your code will run orders of magnitude slower than either package you mentioned. Not just because those libraries are written in much lower-level languages, but also (FFTW in particular) they are written so heavily optimized, taking advantage of cache locality, vector units, and basically every trick in the book, that it would not surprise me if they ran at 10,000x the speed of a naive Python implementation. Even if you are using numpy in your implementation, it will still pale in comparison.

So yes; use numpy's fftpack. If that is not fast enough, you can try the python bindings for FFTW (PyFFTW), but the speedup from fftpack to fftw will not be nearly as dramatic. I really doubt there's a need to drop into C++ just for FFTs - they're sort of the ideal case for Python bindings.

回答2:

If you need speed, then you want to go for FFTW, check out the pyfftw project. In order to use processor SIMD instructions, you need to align the data and there is not an easy way of doing so in numpy. Moreover, pyfftw allows you to use true multithreading, so trust me, it will be much faster.

回答3:

In case you wish to stick to Python (handling and maintaining custom C++ bindings can be time consuming), you have the alternative of using OpenCV's implementation of FFT.

I put together a toy example comparing OpenCV's dft() and numpy's fft2 functions in python (Intel(R) Core(TM) i7-3930K CPU).

samplesFreq_cv2 = [
        cv2.dft(samples[iS])
        for iS in xrange(nbSamples)]

samplesFreq_np = [
        np.fft.fft2(samples[iS])
        for iS in xrange(nbSamples)]

Results for sequentially transforming 20000 image patches of varying resolutions from 20x20 to 60x60:
Numpy's fft2: 1.709100 seconds
OpenCV's dft: 0.621239 seconds

This is likely not as fast as binding to a dedicates C++ library like fftw, but it's a rather low-hanging fruit.

来源：https://stackoverflow.com/questions/28752126/numpy-fft-pack-vs-fftw-vs-implement-dft-on-your-own

标签

python

numpy

fft

fftw