May 2025
Intermediate to advanced
468 pages
6h 20m
Chinese
Content preview from 《高性能 Python》第二版
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第 7 章 编译为 C 语言 编译为 C 语言
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要让代码运行得更快，最简单的方法就是减少代码的工作量。 假设你已经选择了好的算法，并减少了处理的数据量，那么执行更少指令的最简单方法就是将代码编译成机器码。
Python 为此提供了多种选择，包括基于 C 语言的纯编译方法，如Cython；通过Numba 进行的基于 LLVM 的编译；以及替换虚拟机PyPy，其中包括内置的即时 (JIT) 编译器。在决定采用哪种方法时，您需要平衡代码适应性和团队速度的要求。
每种工具都会给你的工具链增加一个新的依赖，Cython 要求你用一种新的语言类型（Python 和 C 的混合语言）编写代码，这意味着你需要一种新的技能。Cython 的新语言可能会影响团队的工作速度，因为不懂 C 语言的团队成员可能难以支持这种代码；但在实践中，这可能是一个小问题，因为你只会在代码中精心选择的小部分区域使用 Cython。
值得注意的是，对代码进行 CPU 和内存剖析可能会让您开始考虑可能应用的更高层次的算法优化。这些算法上的变化（例如避免计算的附加逻辑或避免重新计算的缓存）可以帮助您避免在代码中做不必要的工作，而 Python 的表达性可以帮助您发现这些算法机会。Radim Řehůřek在"Making Deep Learning Fly with RadimRehurek.com（2014）"中讨论了Python实现如何战胜纯C实现。
在本章中，我们将回顾以下内容：
-
Cython，最常用的 C 语言编译工具，涵盖
numpy和普通 Python 代码（需要一定的 C 语言知识） -
Numba，一种专门用于
numpy代码的新编译器 -
PyPy，一种稳定的非
numpy代码即时编译器，可替代正常的 Python 可执行文件
本章后面我们将介绍外来函数接口，它允许将 C 代码编译成 Python 的扩展模块。Python 的本地 API 可与 ctypes 或cffi （来自 PyPy 的作者），以及f2py Fortran 到 Python 的转换器一起使用。
可能获得什么样的速度提升？
如果您的问题适合编译方法，那么速度提高一个数量级或更多是完全可能的。在此，我们将介绍在单核上实现一到两个数量级的提速的各种方法，以及通过OpenMP 使用多核的方法。
编译后运行速度往往更快的 Python 代码是数学代码，它有很多重复相同操作很多次的循环。在这些循环中，您可能会创建大量的临时对象。
调用外部库的代码（如正则表达式、字符串操作和调用数据库库）在编译后不太可能有任何提速。受 I/O 约束的程序也不太可能有明显的速度提升。
同样，如果您的 Python 代码主要是调用矢量化的numpy 例程，那么编译后运行速度可能不会更快--只有当编译的代码主要是 Python 代码（可能主要是循环代码）时运行速度才会更快。我们在第 6 章中介绍了numpy操作 ...
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