使用Cython提升Python程序性能教程

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"Cython教程：提升Python程序执行效率的C语言融合方法" Cython是一种基于Python的编程语言，它引入了额外的语法来提供静态类型声明，旨在结合Python的易用性和C语言的高性能。这个教程是针对8th Python in Science Conference（SciPy 2009）的，由多位来自不同大学和机构的专家共同撰写，主要讨论了如何通过Cython来优化Python代码，以及如何利用Cython与外部C库进行交互。 Cython的核心优势在于，它允许开发者使用Python的高级抽象和动态特性编写代码，同时通过静态类型声明实现接近C语言的执行速度。这使得程序员可以利用Python的便利性进行快速开发，同时在需要的地方提升性能，特别是在科学计算和数据处理等对性能有高要求的领域。 Cython的语法： Cython扩展了Python的基础语法，增加了类型声明。例如，你可以为变量指定特定的数据类型，如`int`、`double`或`numpy.ndarray`。这样的声明让编译器能够生成更高效的机器代码，减少运行时的类型检查和自动内存管理开销。优化Python代码：在Python中，动态类型和垃圾回收机制使得代码易于编写但可能降低执行速度。Cython通过静态类型声明和内联C代码，可以直接将Python函数转换为C扩展，从而加速执行。此外，Cython还支持C++模板和C语言的指针操作，允许直接访问内存，进一步提升性能。接口与C/C++库： Cython使得与C或C++库的交互变得简单。通过`.pxd`文件定义C函数和数据类型，然后在Cython代码中导入这些定义，就可以直接调用C库的函数，而无需使用Python的ctypes或Cython的`cdef extern`关键字。这种方式不仅简化了接口，还减少了潜在的错误。应用实例：在科学计算中，例如在处理大量数组数据时，Cython可以显著提升numpy数组操作的速度。通过将计算密集型的部分代码用Cython重写，可以避免Python解释器的开销，直接利用底层硬件的计算能力。安装与使用：要开始使用Cython，首先需要安装Cython编译器和一个C编译器（如GCC）。然后，将Python源代码文件`.py`重命名为`.pyx`，并使用Cython编译成C源码，再用C编译器编译成Python可以导入的共享库。最后，在Python代码中，像导入普通Python模块一样导入Cython编译后的模块。总结： Cython是Python开发者提升代码性能的重要工具，它提供了Python与C的无缝连接，让开发者能够在保持Python的可读性和简洁性的同时，享受到C语言的高效执行。通过学习和应用Cython，开发者可以在科学计算、数据分析等领域构建出既易于维护又性能卓越的软件系统。

Cython tutorial

Figure 2 Using the -a switch to the cython command

line program (or following a link from the Sage note-

book) results in an HTML report of Cython code

interleaved with the generated C code. Lines are col-

ored according to the level of “typedness” - white

lines translates to pure C without any Python API

calls. This report is invaluable when optimizing a

function for speed.

from math import sin

def f(x):

return sin(x**2)

def integrate_f(a, b, N):

s = 0

dx = (b-a)/N

for i in range(N):

s += f(a+i*dx)

return s * dx

Simply compiling this in Cython merely gives a 5%

speedup. This is better than nothing, but adding some

static types can make a much larger diﬀerence.

With additional type declarations, this might look like:

from math import sin

def f(double x):

return sin(x**2)

def integrate_f(double a, double b, int N):

cdef int i

cdef double s, dx

s = 0

dx = (b-a)/N

for i in range(N):

s += f(a+i*dx)

return s * dx

Since the iterator variable i is typed with C semantics,

the for-loop will be compiled to pure C code. Typing a,

s and dx is important as they are involved in arithmetic

withing the for-loop; typing b and N makes less of a

diﬀerence, but in this case it is not much extra work

to be consistent and type the entire function.

This results in a 24 times speedup over the pure

Python version.

cdef functions

Python function calls can be expensive, and this is

especially true in Cython because one might need to

convert to and from Python objects to do the call. In

our example above, the argument is assumed to be a

C double both inside f() and in the call to it, yet a

Python float object must be constructed around the

argument in order to pass it.

Therefore Cython provides a syntax for declaring a C-

style function, the cdef keyword:

cdef double f(double) except *:

return sin(x**2)

Some form of except-modiﬁer should usually be added,

otherwise Cython will not be able to propagate excep-

tions raised in the function (or a function it calls).

Above except * is used which is always safe. An ex-

cept clause can be left out if the function returns a

Python object or if it is guaranteed that an exception

will not be raised within the function call.

A side-eﬀect of cdef is that the function is no longer

available from Python-space, as Python wouldn’t know

how to call it. Using the cpdef keyword instead of

cdef, a Python wrapper is also created, so that the

function is available both from Cython (fast, passing

typed values directly) and from Python (wrapping val-

ues in Python objects).

Note also that it is no longer possible to change f at

runtime.

Speedup: 45 times over pure Python.

Calling external C functions

It is perfectly OK to do from math import sin to

use Python’s sin() function. However, calling C’s

own sin() function is substantially faster, especially

in tight loops. It can be declared and used in Cython

as follows:

cdef extern from "math.h":

double sin(double)

cdef double f(double x):

return sin(x*x)

At this point there are no longer any Python wrapper

objects around our values inside of the main for loop,

and so we get an impressive speedup to 219 times the

speed of Python.

Note that the above code re-declares the function from

math.h to make it available to Cython code. The C

compiler will see the original declaration in math.h at

compile time, but Cython does not parse “math.h” and

requires a separate deﬁnition.

When calling C functions, one must take care to link

in the appropriate libraries. This can be platform-

speciﬁc; the below example works on Linux and Mac

OS X:

2009, S. Behnel, R. Bradshaw, D. Seljebotn 6

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