numpy.cross()性能不佳 [英] Poor numpy.cross() performance

问题描述

我一直在进行一些性能测试，以提高我正在编写的宠物项目的性能.这是一个非常耗费大量数据的应用程序，因此我一直在与Numpy一起玩，以提高计算性能.

I've been doing some performance testing in order to improve the performance of a pet project I'm writing. It's a very number-crunching intensive application, so I've been playing with Numpy as a way of improving computational performance.

但是，以下性能测试的结果令人惊讶....

However, the result from the following performance tests were quite surprising....

测试源代码 (已更新了用于提升和批量提交的测试用例)

import timeit

numpySetup = """
import numpy
left = numpy.array([1.0,0.0,0.0])
right = numpy.array([0.0,1.0,0.0])
"""

hoistSetup = numpySetup +'hoist = numpy.cross\n'

pythonSetup = """
left = [1.0,0.0,0.0]
right = [0.0,1.0,0.0]
"""

numpyBatchSetup = """
import numpy

l = numpy.array([1.0,0.0,0.0])
left = numpy.array([l]*10000)

r = numpy.array([0.0,1.0,0.0])
right = numpy.array([r]*10000)
"""

pythonCrossCode = """
x = ((left[1] * right[2]) - (left[2] * right[1]))
y = ((left[2] * right[0]) - (left[0] * right[2]))
z = ((left[0] * right[1]) - (left[1] * right[0]))
"""

pythonCross = timeit.Timer(pythonCrossCode, pythonSetup)
numpyCross = timeit.Timer ('numpy.cross(left, right)' , numpySetup)
hybridCross = timeit.Timer(pythonCrossCode, numpySetup)
hoistCross = timeit.Timer('hoist(left, right)', hoistSetup)
batchCross = timeit.Timer('numpy.cross(left, right)', numpyBatchSetup) 

print 'Python Cross Product : %4.6f ' % pythonCross.timeit(1000000)
print 'Numpy Cross Product  : %4.6f ' % numpyCross.timeit(1000000) 
print 'Hybrid Cross Product : %4.6f ' % hybridCross.timeit(1000000) 
print 'Hoist Cross Product  : %4.6f ' % hoistCross.timeit(1000000) 
# 100 batches of 10000 each is equivalent to 1000000
print 'Batch Cross Product  : %4.6f ' % batchCross.timeit(100) 

原始结果

Python Cross Product : 0.754945 
Numpy Cross Product  : 20.752983 
Hybrid Cross Product : 4.467417 

最终结果

Python Cross Product : 0.894334 
Numpy Cross Product  : 21.099040 
Hybrid Cross Product : 4.467194 
Hoist Cross Product  : 20.896225 
Batch Cross Product  : 0.262964 

不用说，这不是我预期的结果.纯Python版本的执行速度比Numpy快30倍.在其他测试中，Numpy的性能要好于Python的等效性能(这是预期的结果).

Needless to say, this wasn't the result I expected. The pure Python version performs almost 30x faster than Numpy. Numpy performance in other tests has been better than the Python equivalent (which was the expected result).

所以，我有两个相关的问题:

So, I've got two related questions:

• 有人能解释为什么NumPy在这种情况下表现这么差吗?
• 我有什么办法可以解决吗?

推荐答案

尝试使用更大的数组.我认为，仅在此处调用numpy方法的开销就超出了Python版本所需的简单几个列表访问权限.如果您处理更大的数组，我认为您会看到numpy的巨大胜利.

Try this with larger arrays. I think that just the cost of calling the methods of numpy here overruns the simple several list accesses required by the Python version. If you deal with larger arrays, I think you'll see large wins for numpy.

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