与 C 数组相比，带有 NumPy 数组内存视图的 Cython 性能较差

我遇到了一个非常奇怪的结果基准 http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day4_python_cython_numba.ipynb?create=1

这些都是冒泡排序实现的不同风格，n=10^4 时最快的方法是在内部将 Python 列表转换为 C 数组。相反，黄线对应于我将 NumPy 数组与内存视图一起使用的代码。我预计结果会是相反的。我（和同事）重复了几次基准测试，但总是得到相同的结果。也许有人知道这里发生了什么......

图中的黑线对应于代码：

%%cython
cimport cython
from libc.stdlib cimport malloc, free

def cython_bubblesort_clist(a_list):
    """ 
    The Cython implementation of bubble sort with internal
    conversion between Python list objects and C arrays.

    """
    cdef int *c_list
    c_list = <int *>malloc(len(a_list)*cython.sizeof(int))
    cdef int count, i, j # static type declarations
    count = len(a_list)

    # convert Python list to C array
    for i in range(count):
        c_list[i] = a_list[i]

    for i in range(count):
        for j in range(1, count):
            if c_list[j] < c_list[j-1]:
                c_list[j-1], c_list[j] = c_list[j], c_list[j-1]

    # convert C array back to Python list
    for i in range(count):
        a_list[i] = c_list[i]

    free(c_list)
    return a_list

以及这段代码的粉色线：

%%cython
import numpy as np
cimport numpy as np
cimport cython
def cython_bubblesort_numpy(long[:] np_ary):
    """ 
    The Cython implementation of bubble sort with NumPy memoryview.

    """
    cdef int count, i, j # static type declarations
    count = np_ary.shape[0]

    for i in range(count):
        for j in range(1, count):
            if np_ary[j] < np_ary[j-1]:
                np_ary[j-1], np_ary[j] = np_ary[j], np_ary[j-1]

    return np.asarray(np_ary)

正如上面评论中所建议的，我添加了装饰器

%%cython
import numpy as np
cimport numpy as np
cimport cython
@cython.boundscheck(False) 
@cython.wraparound(False)
cpdef cython_bubblesort_numpy(long[:] np_ary):
    """ 
    The Cython implementation of bubble sort with NumPy memoryview.

    """
    cdef int count, i, j # static type declarations
    count = np_ary.shape[0]

    for i in range(count):
        for j in range(1, count):
            if np_ary[j] < np_ary[j-1]:
                np_ary[j-1], np_ary[j] = np_ary[j], np_ary[j-1]

    return np.asarray(np_ary)

结果现在更符合我的预期:)