cpython vs cython vs . numpy阵列性能。

I am doing some performance test on a variant of the prime numbers generator from http://docs.cython.org/src/tutorial/numpy.html. The below performance measures are with kmax=1000

我正在从http://docs.cython.org/src/tutorial/numpy.html对质数生成器的一个变体进行性能测试。下面的性能指标是kmax=1000。

Pure Python implementation, running in CPython: 0.15s

纯Python实现，运行于CPython: 0.15s。

Pure Python implementation, running in Cython: 0.07s

纯Python实现，运行在Cython: 0.07s。

def primes(kmax):
    p = []
    k = 0
    n = 2
    while k < kmax:
        i = 0
        while i < k and n % p[i] != 0:
            i = i + 1
        if i == k:
            p.append(n)
            k = k + 1
        n = n + 1
    return p

Pure Python+Numpy implementation, running in CPython: 1.25s

纯Python+Numpy实现，运行于CPython: 1.25s。

import numpy

def primes(kmax):
    p = numpy.empty(kmax, dtype=int)
    k = 0
    n = 2
    while k < kmax:
        i = 0
        while i < k and n % p[i] != 0:
            i = i + 1
        if i == k:
            p[k] = n
            k = k + 1
        n = n + 1
    return p

Cython implementation using int*: 0.003s

Cython实现使用int*: 0.003。

from libc.stdlib cimport malloc, free

def primes(int kmax):
    cdef int n, k, i
    cdef int *p = <int *>malloc(kmax * sizeof(int))
    result = []
    k = 0
    n = 2
    while k < kmax:
        i = 0
        while i < k and n % p[i] != 0:
            i = i + 1
        if i == k:
            p[k] = n
            k = k + 1
            result.append(n)
        n = n + 1
    free(p)
    return result

The above performs great but looks horrible, as it holds two copies of the data... so I tried reimplementing it:

上面的表现很好，但是看起来很糟糕，因为它包含了两个数据的副本……所以我尝试重新实施它:

Cython + Numpy: 1.01s

Cython + Numpy:1.01 s

import numpy as np
cimport numpy as np
cimport cython

DTYPE = np.int
ctypedef np.int_t DTYPE_t

@cython.boundscheck(False)
def primes(DTYPE_t kmax):
    cdef DTYPE_t n, k, i
    cdef np.ndarray p = np.empty(kmax, dtype=DTYPE)
    k = 0
    n = 2
    while k < kmax:
        i = 0
        while i < k and n % p[i] != 0:
            i = i + 1
        if i == k:
            p[k] = n
            k = k + 1
        n = n + 1
    return p

Questions:

问题:

1. why is the numpy array so incredibly slower than a python list, when running on CPython?
2. 当在CPython上运行时，为什么numpy数组要比python列表慢得令人难以置信呢?
3. what did I do wrong in the Cython+Numpy implementation? cython is obviously NOT treating the numpy array as an int[] as it should.
4. 我在Cython+Numpy实现中做错了什么?显然，cython不将numpy数组视为int[]。

5. how do I cast a numpy array to a int*? The below doesn't work

   如何将numpy数组转换为int*?以下是行不通的

   cdef numpy.nparray a = numpy.zeros(100, dtype=int)
   cdef int * p = <int *>a.data
   

3 个解决方案

#1

cdef DTYPE_t [:] p_view = p

Using this instead of p in the calculations. reduced the runtime from 580 ms down to 2.8 ms for me. About the exact same runtime as the implementation using *int. And that's about the max you can expect from this.

用这个代替p。将运行时从580 ms降低到2.8 ms。与使用*int的实现完全相同的运行时。这就是你所能期望的最大值。

DTYPE = np.int
ctypedef np.int_t DTYPE_t

@cython.boundscheck(False)
def primes(DTYPE_t kmax):
    cdef DTYPE_t n, k, i
    cdef np.ndarray p = np.empty(kmax, dtype=DTYPE)
    cdef DTYPE_t [:] p_view = p
    k = 0
    n = 2
    while k < kmax:
        i = 0
        while i < k and n % p_view[i] != 0:
            i = i + 1
        if i == k:
            p_view[k] = n
            k = k + 1
        n = n + 1
    return p

#2

why is the numpy array so incredibly slower than a python list, when running on CPython?

当在CPython上运行时，为什么numpy数组要比python列表慢得令人难以置信呢?

Because you didn't fully type it. Use

因为你没有完全输入。使用

cdef np.ndarray[dtype=np.int, ndim=1] p = np.empty(kmax, dtype=DTYPE)

how do I cast a numpy array to a int*?

如何将numpy数组转换为int*?

By using np.intc as the dtype, not np.int (which is a C long). That's

使用np.intc作为dtype，而不是np.int(这是一个C长)。这是

cdef np.ndarray[dtype=int, ndim=1] p = np.empty(kmax, dtype=np.intc)

(But really, use a memoryview, they're much cleaner and the Cython folks want to get rid of the NumPy array syntax in the long run.)

(但实际上，使用memoryview，它们更干净，而Cython人员希望从长期运行中去掉NumPy数组语法。)

#3

Best syntax I found so far:

迄今为止我发现的最佳语法:

import numpy
cimport numpy
cimport cython

@cython.boundscheck(False)
@cython.wraparound(False)
def primes(int kmax):
    cdef int n, k, i
    cdef numpy.ndarray[int] p = numpy.empty(kmax, dtype=numpy.int32)
    k = 0
    n = 2
    while k < kmax:
        i = 0
        while i < k and n % p[i] != 0:
            i = i + 1
        if i == k:
            p[k] = n
            k = k + 1
        n = n + 1
    return p

Note where I used numpy.int32 instead of int. Anything on the left side of a cdef is a C type (thus int = int32 and float = float32), while anything on the RIGHT side of it (or outside of a cdef) is a python type (int = int64 and float = float64)

注意,我使用numpy.int32代替int。任何cdef的左边是一个C型(因此int = int32和浮动= float32),而任何的右边(或cdef之外)是python类型(int = int64和浮动= float64)