加速Python性能：Cython实战指南

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《Cython：Python性能提升与C/C++库集成指南》是一本由Kurt W. Smith编著的实用手册，专为Python程序员设计，特别是那些关注性能优化的开发者。Cython是一种编译器和混合编程语言，它在诸如NumPy、Pandas、h5py和scikit-learn等关键Python项目中发挥着重要作用，能够将Python的表达力与C和C++的性能和控制性结合起来。书中详述了如何利用Cython实现以下几点： 1. **静态类型提升**：通过Cython的静态类型系统，可以显著加速Python代码的执行速度，减少运行时的解释开销。 2. **数值密集型编程优化**：作者提供了实际操作，让读者了解如何使用Cython的特性来增强Python在处理大量数值计算时的性能。 3. **创建新型类型**：Cython允许开发者创建新的自定义类型，展示出Python面向对象编程在性能上的潜力。 4. **模块和包组织**：学习如何有效地组织Cython代码，将其拆分为独立模块和包，同时保持高效的执行效率。 5. **C/C++库封装**：借助Cython，可以轻松地为Python提供C和C++库的接口，使得这些高性能底层库能无缝融入Python程序。 6. **代码优化工具**：Cython提供了运行时和编译时的性能分析工具，帮助开发者识别并改进代码中的瓶颈。 7. **并行编程支持**：Cython的prange函数允许开发者透明地使用OpenMP并行化循环，提升程序的多核处理能力。这本书适合初学者和进阶者，无论你是刚接触Cython还是想深入理解其在性能优化中的角色，都将从中获益匪浅。通过Kurt Smith的指导，读者将掌握这门技术，使其成为提升Python项目性能的强大武器。此外，该书定价合理，对于任何寻求高性能Python解决方案的开发人员来说，是一本值得投资的资源。

Chapter 12, Parallel Programming with Cython

Where we discover Cython’s prange special function, which allows us to easily turn

on thread-based parallelism and bypass the global interpreter lock.

Chapter 13, Cython in Context

Where we compare Cython with other tools in the same space and indulge in a little

prognostication.

Conventions Used in This Book

The following typographical conventions are used in this book:

Italic

Indicates new terms, URLs, email addresses, filenames, and file extensions.

Constant width

Used for program listings, as well as within paragraphs to refer to program elements

such as variable or function names, data types, statements, and keywords.

Constant width italic

Shows text that should be replaced with user-supplied values or by values deter‐

mined by context.

This element signifies a tip or suggestion.

This element signifies a general note.

This element indicates a warning or caution.

Using Code Examples

Supplemental material and the full source code for the in-text examples is available for

download at https://github.com/cythonbook/examples.

xiv | Preface

All Cython code in this book is tested with Cython versions 0.20.2 and 0.21. The Cython

language and compiler are fairly stable, and the code in this book will likely work with

several earlier and later versions. That said, there is currently no strong backward com‐

patibility constraint for future Cython releases, so some examples may require updating

in the future.

This book is here to help you get your job done. In general, if example code is offered

with this book, you may use it in your programs and documentation. You do not need

to contact us for permission unless you’re reproducing a significant portion of the code.

For example, writing a program that uses several chunks of code from this book does

not require permission. Selling or distributing a CD-ROM of examples from O’Reilly

books does require permission. Answering a question by citing this book and quoting

example code does not require permission. Incorporating a significant amount of ex‐

ample code from this book into your product’s documentation does require permission.

We appreciate, but do not require, attribution. An attribution usually includes the title,

author, publisher, and ISBN. For example: “Cython by Kurt W. Smith, PhD (O’Reilly).

If you feel your use of code examples falls outside fair use or the permission given above,

feel free to contact us at permissions@oreilly.com.

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Preface | xv

How to Contact Us

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Acknowledgments

The Cython core developers—Robert Bradshaw, Stefan Behnel, and Dag Sverre

Seljebotn—and Pyrex’s main developer, Greg Ewing, deserve the lion’s share of praise

for conceiving of such a unique and useful tool, and for shouldering the years of devel‐

opment effort to make it happen. Without your insight, foresight, and diligence, there

would be no reason for this book.

This work emerged rather than being planned, and is the result of several happy cir‐

cumstances. Thanks to my employer, Enthought, and to the SciPy Conference organ‐

izers for providing a platform where Cython can shine. Thanks to Katy Huff (THW

FTW!) for putting a bug in O’Reilly’s ear; thanks to Mike Loukides for giving this book

a hearing and for shepherding it through the proposal process; thanks to Meghan

Blanchette for the encouragement and flexibility.

Thanks is due to all the technical editors for your time and investment: Robert Bradshaw,

Robert Grant, Jonathan Rocher, Jordan Weaver, and Anthony Scopatz. The manuscript

and examples would be half as good without your perspective and input.

Lastly, thanks to my beautiful and talented wife. Little do you realize how much you’ve

had a hand in this work without writing a single word.

xvi | Preface

1. To follow along with the examples in this chapter, please see https://github.com/cythonbook/examples.

Both languages are mainstream, but they are typically used in different domains, given

their differences. Cython’s beauty is this: it combines Python’s expressiveness and dy‐

namism with C’s bare-metal performance while still feeling like Python.

With very few exceptions, Python code (both versions 2.x and 3.x) is already valid

Cython code. Cython adds a small number of keywords to the Python language to tap

into C’s type system, allowing the cython compiler to generate efficient C code. If you

already know Python and have a basic understanding of C or C++, you will be able to

quickly learn Cython. You do not have to learn yet another interface language.

We can think of Cython as two projects in one. If compiling Python to C is Cython’s

yin, then interfacing C or C++ with Python is its yang. We can start with Python code

that needs better performance, or we can start with C (or C++) code that needs an

optimized Python interface. To speed up Python code, Cython compiles Python source

with optional static type declarations to achieve massive performance improvements,

depending on the algorithm. To interface C or C++ libraries with Python, we can use

Cython to interface with external code and create optimized wrappers. Both

capabilities—compiling Python and interfacing with external code—are designed to

work together well, and each is an essential part of what makes Cython useful. With

Cython, we can move in either direction, coming from either starting point.

Cython and CPython

Cython is often confused with CPython (mind the P), but the two are very different.

CPython is the name of the standard and most widely used Python implementation.

CPython’s core is written in the C language, and the C in CPython is meant to distinguish

it from Python the language specification and Python implementations in other lan‐

guages, such as Jython (Java), IronPython (.NET), and PyPy (Python implemented in

Python!). CPython provides a C-level interface into the Python language; the interface

is known as the Python/C API. Cython uses this C interface extensively, and therefore

Cython depends on CPython. Cython is not another implementation of Python—it

needs the CPython runtime to run the extension modules it generates.

Let’s see an example.

Comparing Python, C, and Cython

Consider a simple Python function fib that computes the nth Fibonacci number:

2 | Chapter 1: Cython Essentials

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