Improving Numpy Performance

Question

I\'d like to improve the performance of convolution using python, and was hoping for some insight on how to best go about improving performance.

I am currently usin

Answer 1

Before going to say C with ctypes, I'd suggest running a standalone convolve in C, to see where the limit is.
Similarly for CUDA, cython, scipy.weave ...

Added 7feb: convolve33 8-bit data with clipping takes ~ 20 clock cycles per point, 2 clock cycles per mem access, on my mac g4 pcc with gcc 4.2. Your mileage will vary.

A couple of subtleties:

• do you care about correct clipping to 0..255 ? np.clip() is slow, cython etc. don't know.
• Numpy/scipy may need memory for temps the size of A (so keep 2*sizeof(A) < cache size).
  If your C code, though, does a running update inplace, that's half the mem but a different algorithm.

By the way, google theano convolve => "A convolution op that should mimic scipy.signal.convolve2d, but faster! In development"