Cython初学者-加速numpy广播

Trying Cython for the first time, trying to get a speedup on a function that does subtraction and addition on 2 numpy arrays and a float32. I'm trying to get this function to be as quick as possible it's called a lot of times and if i can speed this up then it's a big win.

第一次尝试Cython，尝试获得对两个numpy数组和一个float32进行减法和加法的函数的加速。我试着让这个函数尽可能快它被调用了很多次如果我能加速它，那就是一个大胜利。

 def broadcast(w, m, spl):
       """
       w and m are float32 ndarrays e.g shape (43,)
       spl is an nd.float32 value e.g 9722.0
       """
        return w + (m - spl)

My cythonising so far is

到目前为止，我的兴奋是

import numpy as np
cimport numpy as np
DTYPE = np.float32
ctypedef np.float32_t DTYPE_t

def broadcast(np.ndarray w, np.ndarray m, np.float32 spl):
    return w + (m - spl)

but it returns the error:

但它返回错误:

'float32' is not a type identifier

I'm not sure how why I can't declare the type? Do I need to declare a C type? What is an np.float32 in C?

我不知道为什么我不能声明类型?我需要声明C类型吗?什么是一个np。在C float32吗?

1 个解决方案

#1

As commented by @YXD, you won't get a speed improvement using Cython to loop over the arrays to perform these operations. Specially for simple operations, Numpy uses SIMD programming which is very efficient.

正如@YXD所评论的那样，使用Cython对数组进行循环来执行这些操作，您不会得到一个速度改进。特别是对于简单的操作，Numpy使用SIMD编程，这是非常有效的。

Despite of that, you could improve the memory usage and the performance by modifying array w in place, if the original array is no longer needed:

尽管如此，如果不再需要原始数组，您可以通过修改适当的数组w来提高内存使用和性能:

def func(w, m, spl)
    w += m
    w -= spl

then, instead of calling:

然后,而不是调用:

out = func(w, m, spl)

the new call:

新电话:

func(w, m, spl)

will store the output inside w.

将输出存储在w中。

#1