python cython c 性能对比

我们用以下方法计算百万以上float型数据的标准偏差，以估计各个方法的计算性能：

• 原始python
• numpy
• cython
• c（由cython调用）

python 原始方法：

 # File: StdDev.py

 import math

 def pyStdDev(a):
     mean = sum(a) / len(a)
     return math.sqrt((sum(((x - mean)**2 for x in a)) / len(a)))

引入numpy对象：

 # File: StdDev.py

 import numpy as np

 def npStdDev(a):
     return np.std(a)

简单cython代码：

# File: cyStdDev.pyx

import math

def cyStdDev(a):
    m = a.mean()
    w = a - m
    wSq = w**2
    return math.sqrt(wSq.mean())

numpy优化后的cython：

# File: cyStdDev.pyx

cdef extern from "math.h":
    double sqrt(double m)

from numpy cimport ndarray
cimport numpy as np
cimport cython

@cython.boundscheck(False)
def cyOptStdDev(ndarray[np.float64_t, ndim=1] a not None):
    cdef Py_ssize_t i
    cdef Py_ssize_t n = a.shape[0]
    cdef double m = 0.0
    for i in range(n):
        m += a[i]
    m /= n
    cdef double v = 0.0
    for i in range(n):
        v += (a[i] - m)**2
    return sqrt(v / n)

最后cython调用”c”代码：

# File: cyStdDev.pyx

cdef extern from "std_dev.h":
    double std_dev(double *arr, size_t siz)

def cStdDev(ndarray[np.float64_t, ndim=1] a not None):
    return std_dev(<double*> a.data, a.size)

“c”代码定义在“std_dev.h”：

 #include <stdlib.h>
 double std_dev(double *arr, size_t siz);

在“std_dev.c”实现：

#include <math.h>

#include "std_dev.h"

double std_dev(double *arr, size_t siz) {
    double mean = 0.0;
    double sum_sq;
    double *pVal;
    double diff;
    double ret;

    pVal = arr;
    for (size_t i = ; i < siz; ++i, ++pVal) {
        mean += *pVal;
    }
    mean /= siz;

    pVal = arr;
    sum_sq = 0.0;
    for (size_t i = ; i < siz; ++i, ++pVal) {
        diff = *pVal - mean;
        sum_sq += diff * diff;
    }
    return sqrt(sum_sq / siz);
}

分别测量其运行时间：

# Pure Python
python3 -m timeit -s "import StdDev; import numpy as np; a = [float(v) for v in range(1000000)]" "StdDev.pyStdDev(a)"
# Numpy
python3 -m timeit -s "import StdDev; import numpy as np; a = np.arange(1e6)" "StdDev.npStdDev(a)"
# Cython - naive
python3 -m timeit -s "import cyStdDev; import numpy as np; a = np.arange(1e6)" "cyStdDev.cyStdDev(a)"
# Optimised Cython
python3 -m timeit -s "import cyStdDev; import numpy as np; a = np.arange(1e6)" "cyStdDev.cyOptStdDev(a)"
# Cython calling C
python3 -m timeit -s "import cyStdDev; import numpy as np; a = np.arange(1e6)" "cyStdDev.cStdDev(a)"

结果：

方法	运行时间(ms)	python做基准	numpy做基准
python	183	1倍	0.03倍
numpy	5.97	31	1
cython	7.76	24	0.8
cython + numpy	2.18	84	2.7
调用c	2.22	82	2.7

总结：

1. numpy优化速度很高，相比于python
2. cython 在非优化状态下居然跟numpy性能差不多，优秀
3. 直接手写c语言是性能很高的，但还是不如cython+numpy，大爷还是厉害

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qsy 23 may 2019