Python高性能编程实战指南：打破速度误区

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"《High Performance Python》是一本专为提升Python在服务端开发中性能的专业书籍，由Micha Gorelick和Ian Ozsvald合著，强调了在Python广泛应用的背景下，如何克服其速度慢的传统观念，揭示高效和可扩展计算策略。作者深入探讨了设计选择背后的理论，帮助读者理解Python实现细节，并提升代码执行效率。本书特别关注于解决高数据量程序中的性能瓶颈，指导读者如何显著加快运行速度，使之适用于多核架构或分布式系统。它不仅提供实用的方法和技术，还包含来自企业实践者的具体解决方案和案例故事，这些故事展示了公司如何成功应对高性能编程的挑战，如并行处理、集群部署和系统的可伸缩性保持可靠性。对于经验丰富的Python开发者来说，这本指南不仅提供了技术层面的提升，还包含了如何避免常见陷阱，以及如何根据业务需求进行优化的实战经验。书中所涵盖的知识范围广泛，从基础性能分析到高级并发策略，旨在帮助读者打造既高效又稳定的Python应用，打破Python“不适合生产环境”的刻板印象。读者在阅读过程中，将不仅能学到技术知识，还能获得实用的工程实践智慧，是提升Python性能开发能力的重要参考资料。"

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Using Code Examples

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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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