加速Python编程：性能优化实战指南

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《高性能Python》是一本实用指南，专为那些希望提升Python代码运行速度和可扩展性的程序员而设计。这本书深入剖析了Python设计选择背后的理论，帮助读者更深层次地理解Python的实现机制。书中不仅关注如何定位和解决代码中的性能瓶颈，还针对大数据量程序提供了优化策略。作者Micha Gorelick和Ian Ozsvald通过本书分享了如何利用多核架构或集群的优势，以及如何构建一个在不牺牲可靠性的情况下可以动态扩展的系统。对于经验丰富的Python开发者，他们将学习到一系列具体问题的解决方案，这些案例来自于在社交媒体分析、生产化机器学习等领域实际应用高性能Python的企业。书中内容涵盖了几个关键主题： 1. **掌握numpy和Cython**：numpy是用于科学计算的强大库，而Cython允许编写C扩展，以提高Python代码的性能。读者将学会如何有效使用这些工具来加速矩阵和向量运算。 2. **利用profiler**：通过性能剖析器，开发者能识别CPU时间和内存使用中的瓶颈，以便针对性地优化代码。 3. **数据结构的选择**：了解不同数据结构对性能的影响，根据任务需求选择最适合的数据结构是提高效率的关键。 4. **代码编译**：探索将Python代码转换为机器码的工具，这有助于消除解释型语言的性能限制。 5. **并发管理**：在处理I/O和计算操作时，如何有效地并行执行，以减少等待时间。 6. **多进程编程与分布式计算**：将多进程代码移植到本地或远程集群上，实现更高效的并行工作负载。 7. **内存管理优化**：在解决大型问题时，如何减少内存消耗，保持程序的高效运行。《高性能Python》是一本实用的教程，适合希望通过深入了解Python底层机制和实践技巧，解决实际应用中性能挑战的开发者。通过阅读这本书，你不仅能提升代码的运行速度，还能为你的项目带来更好的可扩展性和可靠性。

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Using Code Examples

Supplemental material (code examples, exercises, etc.) is available for download at

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xiv | Preface

addition, each of these units has different properties that we can use to understand them.

The computational unit has the property of how many computations it can do per

second, the memory unit has the properties of how much data it can hold and how fast

we can read from and write to it, and finally the connections have the property of how

fast they can move data from one place to another.

Using these building blocks, we can talk about a standard workstation at multiple levels

of sophistication. For example, the standard workstation can be thought of as having a

central processing unit (CPU) as the computational unit, connected to both the random

access memory (RAM) and the hard drive as two separate memory units (each having

different capacities and read/write speeds), and finally a bus that provides the connec‐

tions between all of these parts. However, we can also go into more detail and see that

the CPU itself has several memory units in it: the L1, L2, and sometimes even the L3

and L4 cache, which have small capacities but very fast speeds (from several kilobytes

to a dozen megabytes). These extra memory units are connected to the CPU with a

special bus called the backside bus. Furthermore, new computer architectures generally

come with new configurations (for example, Intel’s Nehalem CPUs replaced the front‐

side bus with the Intel QuickPath Interconnect and restructured many connections).

Finally, in both of these approximations of a workstation we have neglected the network

connection, which is effectively a very slow connection to potentially many other com‐

puting and memory units!

To help untangle these various intricacies, let’s go over a brief description of these fun‐

damental blocks.

Computing Units

The computing unit of a computer is the centerpiece of its usefulness—it provides the

ability to transform any bits it receives into other bits or to change the state of the current

process. CPUs are the most commonly used computing unit; however, graphics

processing units (GPUs), which were originally typically used to speed up computer

graphics but are becoming more applicable for numerical applications, are gaining

popularity due to their intrinsically parallel nature, which allows many calculations to

happen simultaneously. Regardless of its type, a computing unit takes in a series of bits

(for example, bits representing numbers) and outputs another set of bits (for example,

representing the sum of those numbers). In addition to the basic arithmetic operations

on integers and real numbers and bitwise operations on binary numbers, some com‐

puting units also provide very specialized operations, such as the “fused multiply add”

operation, which takes in three numbers, A,B,C, and returns the value A * B + C.

The main properties of interest in a computing unit are the number of operations it can

do in one cycle and how many cycles it can do in one second. The first value is measured

2 | Chapter 1: Understanding Performant Python

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