提升Python运行速度的几款工具 📅 发布时间:2026/7/9 5:36:41 👁️ 浏览次数: 尽管Python近年来备受欢迎且极具便利性但广大Python用户都明白在涉及CPU密集型任务时其执行速度相较于C、Java或JavaScript等语言会慢得多速度差异甚至可以达到几个数量级。但是有几个项目吸纳Python的所有优点的同时决定从内到外提升它的性能。如果你想让Python在相同的硬件上运行得更快你有两个基本选项每个选项都有一个缺点。你可以创建一个语言所使用的默认运行时的替代品 (例如CPython)--这是个大工程你可以重写现有的Python代码对其进行速度优化这意味着程序员要做更多的工作以下是六种提高Python性能的方法。每一种都使用了这两种方法中的一种或者是两种方法的结合。PyPy在替代CPython的候选者中PyPy是其中较为突出的一款。它也最有可能成为默认版本因为它与现有的 Python 代码高度兼容。PyPy使用的是及时编译JIT谷歌Chrome的V8 JavaScript引擎也使用同样的技术来加快该语言的速度。虽然PyPy曾经偏爱于Python 2而不是Python 3但PyPy的最新版本支持Python 3.6和Python 3.7以及Python 2.7。但PyPy有一个明显的缺点PyPy没有很好地集成用于加速Python性能的常用库如NumPy。然而最近的版本在很大程度上解决了这个问题。PyPy仍有其他限制它最适合像服务器这样的长期运行程序而临时运行的脚本因为它的性能优势要在一些预热时间之后才会真正体现出来。而且它的可执行文件比CPython占用的空间更大。PystonPyston项目最初是由Dropbox创建的但后来被重写也使用JIT来加快Python的速度。它最初的化身使用了LLVM编译器基础架构来实现这一目的但重写后放弃了LLVM而采用了开销更低的hand-rolled汇编器。重写也使用CPython代码作为项目的基础所以它与传统Python的开箱即用兼容性更高。Pyston的速度还不是很显著--平均快了 20% 左右--但这个项目还处于起步阶段。Nuitka还有一些思路并不是为了优化和提升Python的运行时间而是完全放弃对于它的优化。进而寻求将Python代码移植到本地高速运行的语言中。Nuitka就利用了这一思路它可以将Python转换为C代码并且可以自动打包所有CPython运行时所需的文件。Nuitka的长期计划包括允许Nuitka编译的Python直接与C代码接口从而实现更高的速度。CythonCythonPython的C扩展是一种将代码编译成C语言并提供与C/C代码接口的版本这是为Python编写C扩展的一种方式它将C或C代码包装起来并赋予它一个简单的Python接口Cython也可以用来增量加速Python函数。它的缺点是Cython使用自己特有的语法来发挥它的作用这也给移植代码带来一定困难。尽管如此为了速度Cython 提供了前几个Python版本中没有的优势例如利用C语言中的变量类型。许多Python的科学软件包如Scikit-learn都借鉴了Cython的这种特性以保持操作的精简和快速。NumbaNumba结合了前面两种方法。像Cython一样它加快了语言中最需要的部分通常是CPU绑定的数学运算像PyPy和Pyston一样它使用JIT编译。用Numba编译的函数可以用装饰器指定使用非常简单。此外Numba与它我们熟悉的库配合得很好比如NumPy。typed_pythontyped_python 项目是 A Priori 投资公司支持的一个新生事物它采用了不同于上述任何一个项目的方法。它为Python提供了一个强类型数据结构的集合这些数据结构的类型受到限制。例如我们可以创建一个只接受整数的列表。有了它我们就可以生成高度优化的代码这些代码运行得更快并尽可能地利用处理器的并行性。我们可以用传统的 Python 编写大部分程序然后在一个特定的函数中使用 typed_python 来加速其操作这就像 Cython 可以用来选择性的加速那些可能成为瓶颈的部分一样。结语Python之父Guido van Rossum 坚定地认为Python 的许多性能问题都可以追溯到对该语言的不当使用。例如CPU负荷大的处理可以通过这里提到的一些方法来加速使用NumPy (用于数学与运算)使用多处理扩展调用外部C代码从而避免全局解释器锁(GIL)GIL是Python慢的根源但由于目前还没有可行的方法来替代 Python 中的 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