我试图按照
Continuum Analytics blog基准测试Python,Cython,Numba上给出的一个例子来计算使用for循环计算的总和.不幸的是,我发现Cython比Python慢！
这是我的Python函数定义：
def python_sum(y):
N = len(y)
x = y[0]
for i in xrange(1,N):
x += y[i]
return x
现在我的Cython功能：
def cython_sum(int[:] y):
cdef int N = y.shape[0]
cdef int x = y[0]
cdef int i
for i in xrange(1,N):
x += y[i]
return x
import timeit import numpy as np import cython_sum import python_sum b = np.ones(10000) timer = timeit.Timer(stmt='python_sum.python_sum(b)',setup='from __main__ import python_sum,b') print "Python Sum (ms): %g" % (timer.timeit(1)*1000) timer = timeit.Timer(stmt='cython_sum.cython_sum(b)',setup='from __main__ import cython_sum,b') print "Cython (ms): %g" % (timer.timeit(1)*1000)
现在我的输出是：
Python Sum (ms): 9.44624 Cython (ms): 8.54868
基于上面链接的博客文章中的图表,我期待速度提高100倍-1000倍,但我所看到的只是Cython比vanilla Python略快.
我在这里做错了吗？这似乎是一个非常基本的问题,具有简单的函数定义,很明显很多人使用Cython取得了巨大的成功,所以很明显错误必须在于我.任何人都可以对此有所了解并告诉我我做错了什么？
谢谢！
解决方法
我不确定你为什么会得到那个结果.正如一位评论者所说,你的代码原样不应该工作,因为你要将浮点数传递给一个期望整数的函数.也许你在同一目录中留下了一个cython_sum.py文件？
我做了以下.我创建了一个python_sum.py,其中包含您对python_sum的确切定义.然后我稍微修改了你的Cython代码：
cython_sum.pyx：
def cython_sum(long[:] y): #changed `int` to `long`
cdef int N = y.shape[0]
cdef int x = y[0]
cdef int i
for i in xrange(1,N):
x += y[i]
return x
我创建了一个安装文件来构建Cython模块：
setup.py：
from distutils.core import setup
from Cython.Build import cythonize
setup(
name = 'Cython sum test',ext_modules = cythonize("cython_sum.pyx"),)
我使用python setup.py build_ext –inplace构建了模块.接下来,我对您的测试代码进行了一些修改：
test.py：
import timeit import numpy as np import cython_sum import python_sum # ** added dtype=np.int to create integers ** b = np.ones(10000,dtype=np.int) # ** changed .timeit(1) to .timeit(1000) for each one ** timer = timeit.Timer(stmt='python_sum.python_sum(b)',b') print "Python Sum (ms): %g" % (timer.timeit(1000)*1000) timer = timeit.Timer(stmt='cython_sum.cython_sum(b)',b') print "Cython (ms): %g" % (timer.timeit(1000)*1000)
我得到了以下结果：
$python test.py Python Sum (ms): 4111.74 Cython (ms): 7.06697
现在这是一个很好的加速！
此外,按照here所述的指导原则,我可以获得额外的(小)加速：
cython_fast_sum.pyx：
import numpy as np
cimport numpy as np
DTYPE = np.int
ctypedef np.int_t DTYPE_t
def cython_sum(np.ndarray[DTYPE_t,ndim=1] y):
cdef int N = y.shape[0]
cdef int x = y[0]
cdef int i
for i in xrange(1,N):
x += y[i]
return x
setup_fast.py：
from distutils.core import setup
from Cython.Build import cythonize
import numpy as np
setup(
name = 'Cython fast sum test',ext_modules = cythonize("cython_fast_sum.pyx"),include_dirs = [np.get_include()],)
test.py：
import timeit import numpy as np import cython_sum import cython_fast_sum b = np.ones(10000,dtype=np.int) # ** note 100000 runs,not 1000 ** timer = timeit.Timer(stmt='cython_sum.cython_sum(b)',b') print "Cython naive (ms): %g" % (timer.timeit(100000)*1000) timer = timeit.Timer(stmt='cython_fast_sum.cython_sum(b)',setup='from __main__ import cython_fast_sum,b') print "Cython fast (ms): %g" % (timer.timeit(100000)*1000)
结果：
$python test.py Cython naive (ms): 676.437 Cython fast (ms): 645.797