为什么 numpy.array 这么慢? [英] Why is numpy.array so slow?

问题描述

我对此感到困惑

def main():
    for i in xrange(2560000):
        a = [0.0, 0.0, 0.0]

main()

$ time python test.py

real     0m0.793s

现在让我们用 numpy 来看看:

Let's now see with numpy:

import numpy

def main():
    for i in xrange(2560000):
        a = numpy.array([0.0, 0.0, 0.0])

main()

$ time python test.py

real    0m39.338s

神圣的 CPU 循环蝙蝠侠！

Holy CPU cycles batman!

使用 numpy.zeros(3) 有所改进，但仍然不够恕我直言

Using numpy.zeros(3) improves, but still not enough IMHO

$ time python test.py

real    0m5.610s
user    0m5.449s
sys 0m0.070s

numpy.version.version = '1.5.1'

numpy.version.version = '1.5.1'

如果您想知道在第一个示例中是否跳过了列表创建以进行优化，则不是:

If you are wondering if the list creation is skipped for optimization in the first example, it is not:

  5          19 LOAD_CONST               2 (0.0)
             22 LOAD_CONST               2 (0.0)
             25 LOAD_CONST               2 (0.0)
             28 BUILD_LIST               3
             31 STORE_FAST               1 (a)

推荐答案

Numpy 针对大量数据进行了优化.给它一个 3 长度的小数组，不出所料，它的性能很差.

Numpy is optimised for large amounts of data. Give it a tiny 3 length array and, unsurprisingly, it performs poorly.

考虑单独测试

import timeit

reps = 100

pythonTest = timeit.Timer('a = [0.] * 1000000')
numpyTest = timeit.Timer('a = numpy.zeros(1000000)', setup='import numpy')
uninitialised = timeit.Timer('a = numpy.empty(1000000)', setup='import numpy')
# empty simply allocates the memory. Thus the initial contents of the array 
# is random noise

print 'python list:', pythonTest.timeit(reps), 'seconds'
print 'numpy array:', numpyTest.timeit(reps), 'seconds'
print 'uninitialised array:', uninitialised.timeit(reps), 'seconds'

输出是

python list: 1.22042918205 seconds
numpy array: 1.05412316322 seconds
uninitialised array: 0.0016028881073 seconds

似乎是数组的清零一直在为 numpy 花费时间.因此，除非您需要初始化数组，否则请尝试使用 empty.

It would seem that it is the zeroing of the array that is taking all the time for numpy. So unless you need the array to be initialised then try using empty.

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