改善Numpy表现 [英] Improving Numpy Performance

问题描述

我想使用python改善卷积性能，并希望对如何最好地改善性能有一些见解.

我目前正在使用scipy进行卷积，使用的代码类似于下面的代码段:

import numpy
import scipy
import scipy.signal
import timeit

a=numpy.array ( [ range(1000000) ] )
a.reshape(1000,1000)
filt=numpy.array( [ [ 1, 1, 1 ], [1, -8, 1], [1,1,1] ] )

def convolve():
  global a, filt
  scipy.signal.convolve2d ( a, filt, mode="same" )

t=timeit.Timer("convolve()", "from __main__ import convolve")
print "%.2f sec/pass" % (10 * t.timeit(number=10)/100)

我正在使用灰度级(0到255之间的整数值)处理图像数据，并且当前每个卷积得到大约四分之一秒.我的想法是执行以下操作之一:

使用corepy，最好进行一些优化 用icc&重新编译numpy ikml. 使用python-cuda.

我想知道是否有人对这些方法有任何经验(通常会获得什么样的收益，并且值得花时间)，或者是否有人知道有更好的库可以与Numpy进行卷积. /p>

谢谢！

通过使用Numpy在C语言中重写python循环，可将速度提高约10倍.

解决方案

scipy中用于进行2d卷积的代码有点混乱且未优化.参见 http://svn.scipy.org/svn/scipy/如果您想一窥scipy的底层功能，则可以使用trunk/scipy/signal/firfilter.c .

如果您只想使用一个小而恒定的内核(如您所示的内核)进行处理，则可能会执行以下功能:

def specialconvolve(a):
    # sorry, you must pad the input yourself
    rowconvol = a[1:-1,:] + a[:-2,:] + a[2:,:]
    colconvol = rowconvol[:,1:-1] + rowconvol[:,:-2] + rowconvol[:,2:] - 9*a[1:-1,1:-1]
    return colconvol

该函数利用了上面建议的DarenW之类的内核可分离性，并利用了更优化的numpy算术例程.根据我的测量，它比convolve2d函数快1000倍以上.

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 using scipy to perform the convolution, using code somewhat like the snippet below:

import numpy
import scipy
import scipy.signal
import timeit

a=numpy.array ( [ range(1000000) ] )
a.reshape(1000,1000)
filt=numpy.array( [ [ 1, 1, 1 ], [1, -8, 1], [1,1,1] ] )

def convolve():
  global a, filt
  scipy.signal.convolve2d ( a, filt, mode="same" )

t=timeit.Timer("convolve()", "from __main__ import convolve")
print "%.2f sec/pass" % (10 * t.timeit(number=10)/100)

I am processing image data, using grayscale (integer values between 0 and 255), and I currently get about a quarter of a second per convolution. My thinking was to do one of the following:

Use corepy, preferably with some optimizations Recompile numpy with icc & ikml. Use python-cuda.

I was wondering if anyone had any experience with any of these approaches ( what sort of gain would be typical, and if it is worth the time ), or if anyone is aware of a better library to perform convolution with Numpy.

Thanks!

EDIT:

Speed up of about 10x by re-writing python loop in C over using Numpy.

解决方案

The code in scipy for doing 2d convolutions is a bit messy and unoptimized. See http://svn.scipy.org/svn/scipy/trunk/scipy/signal/firfilter.c if you want a glimpse into the low-level functioning of scipy.

If all you want is to process with a small, constant kernel like the one you showed, a function like this might work:

def specialconvolve(a):
    # sorry, you must pad the input yourself
    rowconvol = a[1:-1,:] + a[:-2,:] + a[2:,:]
    colconvol = rowconvol[:,1:-1] + rowconvol[:,:-2] + rowconvol[:,2:] - 9*a[1:-1,1:-1]
    return colconvol

This function takes advantage of the separability of the kernel like DarenW suggested above, as well as taking advantage of the more optimized numpy arithmetic routines. It's over 1000 times faster than the convolve2d function by my measurements.

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