python for循环每次迭代都会变慢(python for-loop slower each iteration)

 我正在尝试优化一些python代码（以加快一些矩阵操作），我的代码类似于这个（我的真实数据集也类似于'gps'）， 
 
import numpy as np
gps = [np.random.rand(50,50) for i in xrange(1000)]
ips = np.zeros( (len(gps),len(gps)), dtype='float32')

for i in xrange(len(gps)):
  for j in xrange(0,i+1):
    ips[i,j]= f.innerProd(gps[i],gps[j])
    ips[j,i]= ips[i,j]
   print "Inner product matrix: %3.0f %% done (%d of %d)"%  \
               (((i+1)**2.)/(len(gps)**2.)*100, i, len(gps))

def innerProd(mat1,mat2):
    return float(np.sum(np.dot(np.dot(mat1,mat2),mat1)))
 
 我想要理解的是，为什么程序在第一次迭代期间开始快速运行，然后在进一步迭代时减慢？ 我知道这个问题可能有点天真但我真的想在我尝试其他任何事情之前更清楚地了解发生了什么。 我已经在Fortran中实现了我的函数（在Fortran域中留下任何for循环）并使用f2py创建一个动态lib来从python调用函数，这将是python中的新代码。 
 
import numpy as np
import myfortranInnProd as fip

gps = [np.random.rand(50,50) for i in xrange(1000)]
ips = np.zeros( (len(gps),len(gps)), dtype='float32')

ips = fip.innerProd(gps)
 
 不幸的是，我发现（令人惊讶的是）我的fortran-python版本运行速度比第一个版本慢1.5~2倍（重要的是要提到我在Fortran实现上使用了MATMUL（））。 我一直在谷歌搜索一段时间，我相信这种“减速”与内存带宽，内存分配或缓存有关，考虑到大型数据集，但我不太确定背后真正发生的事情以及如何我可以改善表现吗？ 我已经在小型英特尔原子，2GB内存和4核英特尔至强处理器上运行代码，8GB（当然还有相应的缩放数据集）并且“减速”行为是相同的。 
 
 我只需要理解为什么这种“减速”会发生？ 如果我在C中实现该功能，它会有用吗？ 或尝试实现它在GPU上运行？ 还有其他想法如何改进吗？ 提前致谢 

I am trying to optimize some python code (to speed up some matrix operations), my code is something similar to this one (my real dataset is also similar to 'gps'),
 
import numpy as np
gps = [np.random.rand(50,50) for i in xrange(1000)]
ips = np.zeros( (len(gps),len(gps)), dtype='float32')

for i in xrange(len(gps)):
  for j in xrange(0,i+1):
    ips[i,j]= f.innerProd(gps[i],gps[j])
    ips[j,i]= ips[i,j]
   print "Inner product matrix: %3.0f %% done (%d of %d)"%  \
               (((i+1)**2.)/(len(gps)**2.)*100, i, len(gps))

def innerProd(mat1,mat2):
    return float(np.sum(np.dot(np.dot(mat1,mat2),mat1)))
 
What I would like to understand is , why is it that the program begins running fast during the first iterations and then slows down as it iterates further? I know the question might be a bit naive but I really want to have a clearer idea of what is happening before I attempt anything else. I already implemented my function in Fortran (leaving within the Fortran realm any for loops) and used f2py to create a dynamic lib to call the function from python, this would be the new code in python..
 
import numpy as np
import myfortranInnProd as fip

gps = [np.random.rand(50,50) for i in xrange(1000)]
ips = np.zeros( (len(gps),len(gps)), dtype='float32')

ips = fip.innerProd(gps)
 
unfortunately I only found out (surprisingly) that my fortran-python version runs 1.5 ~ 2 times slower than the first version (it is important to mention that I used MATMUL() on the Fortran implementation). I have been googling around for a while and I believe that this "slow down" has something to do with the memory bandwidth, memory allocation or caching, given the large datasets, but I am not very sure about what is really happening behind and how could I improve the performance. I have run the code on both a small intel atom , 2GB ram and a 4 core intel xeon, with 8GB (of course with a correspondingly scaled dataset) and the "slow down" behavior is the same. 
 
I just need to understand why is it that this 'slow down' happens? would it do any good if i implement the function in C ? or try to implement it to run on a GPU ? Any other ideas how to improve it? Thanks in advance

原文：https://stackoverflow.com/questions/5845826