python cython c 性能對比

我們用以下方法計算百萬以上float型數據的標准偏差，以估計各個方法的計算性能：

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

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對象：

# File: StdDev.py

import numpy as np

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

簡單cython代碼：

# File: cyStdDev.pyx

import math

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

numpy優化后的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調用”c”代碼：

# 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”代碼定義在“std_dev.h”：

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

在“std_dev.c”實現：

#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 = 0; i < siz; ++i, ++pVal) {
        mean += *pVal;
    }
    mean /= siz;

    pVal = arr;
    sum_sq = 0.0;
    for (size_t i = 0; i < siz; ++i, ++pVal) {
        diff = *pVal - mean;
        sum_sq += diff * diff;
    }
    return sqrt(sum_sq / siz);
}

分別測量其運行時間：

# 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)"

結果：

方法	運行時間(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
調用c	2.22	82	2.7

總結：

1. numpy優化速度很高，相比於python
2. cython 在非優化狀態下居然跟numpy性能差不多，優秀
3. 直接手寫c語言是性能很高的，但還是不如cython+numpy，大爺還是厲害

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qsy 23 may 2019