Question

我在python中启动了一个项目，主要由循环组成。几天前我读到了cython，它可以帮助你通过静态输入获得更快的代码。我开发了这两个函数来检查性能（一个在python中，另一个在cython中）：

import numpy as np
from time import clock

size = 11
board = np.random.randint(2, size=(size, size))

def py_playout(board, N):
    black_rave = []
    white_rave = []
    for i in range(N):
        for x in range(board.shape[0]):
            for y in range(board.shape[1]):
                if board[(x,y)] == 0:
                    black_rave.append((x,y))
                else:
                    white_rave.append((x,y))
    return black_rave, white_rave

cdef cy_playout(board, int N):
    cdef list white_rave = [], black_rave = []
    cdef int M = board.shape[0], L = board.shape[1]
    cdef int i=0, x=0, y=0
    for i in range(N):
        for x in range(M):
            for y in range(L):
                if board[(x,y)] == 0:
                    black_rave.append((x,y))
                else:
                    white_rave.append((x,y))
    return black_rave, white_rave

我使用下面的代码来测试性能：

t1 = clock()
a = playout(board, 1000)
t2 = clock()
b = playout1(board, 1000)
t3 = clock()

py = t2 - t1
cy = t3 - t2
print('cy is %a times better than py'% str(py / cy))

但是我没有发现任何明显的改进。我还没有使用过Typed-Memoryviews。任何人都可以提出有用的解决方案来提高速度或帮助我使用typed-memoryview重写代码吗？

Answer 1

你是对的，没有在cython函数中为board参数添加类型，加速并不是那么多：

%timeit py_playout(board, 1000)
# 321 ms ± 19.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
%timeit cy_playout(board, 1000)
# 186 ms ± 541 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

但它仍然是两个更快的因素。通过添加类型，例如

cdef cy_playout(int[:, :] board, int N):
    # ...

# or if you want explicit types:
# cimport numpy as np
# cdef cy_playout(np.int64_t[:, :] board, int N):  # or np.int32_t

它更快（几乎快10倍）：

%timeit cy_playout(board, 1000)
# 38.7 ms ± 1.84 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

我还使用timeit（好的IPython魔法%timeit）来获得更准确的时间。

请注意，您还可以使用numba来实现极佳的加速，而无需任何额外的静态输入：

import numba as nb

nb_playout = nb.njit(py_playout)  # Just decorated your python function

%timeit nb_playout(board, 1000)
# 37.5 ms ± 154 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

Answer 2

我实现了一个运行得更快的功能。我只是将black_rave和white_rave声明为内存视图，并将它们放在返回值中：

cdef tuple cy_playout1(int[:, :] board, int N):
    cell_size = int((size ** 2) / 2) + 10
    cdef int[:, :] black_rave = np.empty([cell_size, 2], dtype=np.int32)
    cdef int[:, :] white_rave = np.empty([cell_size, 2], dtype=np.int32)

    cdef int i, j, x, y, h
    i, j = 0, 0
    cdef int M,L
    M = board.shape[0]
    L = board.shape[1]
    for h in range(N):
        for x in range(M):
            for y in range(L):
                if board[x,y] == 0:
                    black_rave[i][0], black_rave[i][1] = x, y
                    i += 1
                elif board[x,y] == 1:
                    white_rave[j][0], white_rave[j][1] = x, y
                    j += 1
        i = 0
        j = 0

    return black_rave[:i], white_rave[:j]

这是速度测试结果：

%timeit py_playout(board, 1000)
%timeit cy_playout(board, 1000)
%timeit cy_playout1(board, 1000)
# 1 loop, best of 3: 200 ms per loop
# 100 loops, best of 3: 9.26 ms per loop
# 100 loops, best of 3: 4.88 ms per loop

如何在cython中实现更好的循环速度性能？

2 个答案: