高能Python

《高能Python》是2015年東南大學出版社出版的圖書。

基本介紹

• 中文名：高能Python
• 作者：Micha Gorelick、Ian Ozsvald
• 出版社：東南大學出版社
• 出版時間：2015年
• 開本：16 開
• 裝幀：平裝
• ISBN：9787564153854

內容簡介

你的Python代碼也許運行正確，但是你需要運行得更快速。通過探討隱藏在設計備選方案中的基礎理論，戈雷利克和歐日沃爾德編著的《高性能Python》將幫助你更深入地理解Python的實現。你將了解如何定位性能瓶頸，從而顯著提升高數據流量程式中的代碼執行效率。

你該如何利用多核架構和集群？或者你該如何搭建一個可以自由伸縮而不會影響可靠性的系統？有經驗的Python程式設計師將會學習到這類問題的具體解決方案，以及來自於各個公司的如何把高性能Python用於社交媒體分析、產品機器學習和其他場景中去的曲折故事。

圖書目錄

Preface

1. Understanding Performant Python

　The Fundamental Computer System

　Computing Units

　Memory Units

　Communications Layers

　Putting the Fundamental Elements Together

　Idealized Computing Versus the Python Virtual Machine

　So Why Use Python?

2. Profiling to Find Bottlenecks

　Profiling Efficiently

　Introducing the Julia Set

　Calculating the Full Julia Set

　Simple Approaches to Timing--print and a Decorator

　Simple Timing Using the Unix time Command

　Using the cProfile Module

　Using runsnakerun to Visualize cProfile Output

　Using line_profiler for Line-by-Line Measurements

　Using memory_profiler to Diagnose Memory Usage

　Inspecting Objects on the Heap with heapy

　Using dowser for Live Graphing of Instantiated Variables

　Using the dis Module to Examine CPython Bytecode

　Different Approaches, Different Complexity

　Unit Testing During Optimization to Maintain Correctness

　No-op @profile Decorator

　Strategies to Profile Your Code Successfully

　Wrap-Up

3. Lists and Tuples

　A More Efficient Search

　Lists Versus Tuples

　Lists as Dynamic Arrays

　Tuples As Static Arrays

　Wrap-Up

4. Dictionaries and Sets

　How Do Dictionaries and Sets Work?

　Inserting and Retrieving

　Deletion

　Resizing

　Hash Functions and Entropy

　Dictionaries and Namespaces

　Wrap-Up

5. Iterators and Generators

　Iterators for Infinite Series

　Lazy Generator Evaluation

　Wrap-Up

6. Matrix and Vector Computation

　Introduction to the Problem

　Aren't Python Lists Good Enough?

　Problems with Allocating Too Much

　Memory Fragmentation

　Understanding perf

　Making Decisions with perf's Output

　Enter numpy

　Applying numpy to the Diffusion Problem

　Memory Allocations and In-Place Operations

　Selective Optimizations: Finding What Needs to Be Fixed

　numexpr: Making In-Place Operations Faster and Easier

　A Cautionary Tale: Verify "Optimizations" (scipy)

　Wrap-Up

7. Compiling to C

　What Sort of Speed Gains Are Possible?

　JIT Versus AOT Compilers

　Why Does Type Information Help the Code Run Faster?

　Using a C Compiler

　Reviewing the Julia Set Example

　Cvthon

　Compiling a Pure-Python Version Using Cython

　Cython Annotations to Analyze a Block of Code

　Adding Some Type Annotations

　Shed Skin

　Building an Extension Module

　The Cost of the Memory Copies

　Cython and numpy

　ParaUelizing the Solution with OpenMP on One Machine

　Numba

　Pythran

　PyPy

　Garbage Collection Differences

　Running PyPy and Installing Modules

　When to Use Each Technology

　Other Upcoming Projects

　A Note on Graphics Processing Units (GPUs)

　A Wish for a Future Compiler Project

　Foreign Function Interfaces

　ctypes

　cffi

　f2py

　CPython Module

　Wrap-Up

8. Concurrency

　Introduction to Asynchronous Programming

　Serial Crawler

　gevent

　tornado

　AsyncIO

　Database Example

　Wrap-Up

9. lhe multiprocessing Module

　An Overview of the Multiprocessing Module

　Estimating Pi Using the Monte Carlo Method

　Estimating Pi Using Processes and Threads

　Using Python Objects

　Random Numbers in Parallel Systems

　Using numpy

　Finding Prime Numbers

　Queues of Work

　Verifying Primes Using Interprocess Communication

　Serial Solution

　Naive Pool Solution

　A Less Naive Pool Solution

　Using Manager.Value as a Flag

　Using Redis as a Flag

　Using RawValue as a Flag

　Using mmap as a Flag

　Using mmap as a Flag Redux

　Sharing numpy Data with multiprocessing

　Synchronizing File and Variable Access

　File Locking

　Locking a Value

　Wrap-Up

10. Clusters and Job Queues

　Benefits of Clustering

　Drawbacks of Clustering

　$462 Million Wall Street Loss Through Poor Cluster Upgrade Strategy

　Skype's 24-Hour Global Outage

　Common Cluster Designs

　How to Start a Clustered Solution

　Ways to Avoid Pain When Using Clusters

　Three Clustering Solutions

　Using the Parallel Python Module for Simple Local Clusters

　Using IPython Parallel to Support Research

　NSQ for Robust Production Clustering

　Queues

　Pub/sub

　Distributed Prime Calculation

　Other Clustering Tools to Look At

　Wrap-Up

11. Using Less RAM

　Objects for Primitives Are Expensive

　The Array Module Stores Many Primitive Objects Cheaply

　Understanding the RAM Used in a Collection

　Bytes Versus Unicode

　Efficiently Storing Lots of Text in RAM

　Trying These Approaches on 8 Million Tokens

　Tips for Using Less RAM

　Probabilistic Data Structures

　Very Approximate Counting with a 1-byte Morris Counter

　K-Minimum Values

　Bloom Filters

　LogLog Counter

　Real-World Example

12. Lessons from the Field

　Adaptive Lab's Social Media Analytics (SOMA)

　Python at Adaptive Lab

　SoMA's Design

　Our Development Methodology

　Maintaining SoMA

　Advice for Fellow Engineers

　Making Deep Learning Fly with RadimRehurek.com

　The Sweet Spot

　Lessons in Optimizing

　Wrap-Up

　Large-Scale Productionized Machine Learning at Lyst.com

　Pythons Place at Lyst

　Cluster Design

　Code Evolution in a Fast-Moving Start-Up

　Building the Recommendation Engine

　Reporting and Monitoring

　Some Advice

　Large-Scale Social Media Analysis at Smesh

　Pythons Role at Smesh

　The Platform

　High Performance Real-Time String Matching

　Reporting, Monitoring, Debugging, and Deployment

　PyPy for Successful Web and Data Processing Systems

　Prerequisites

　The Database

　The Web Application

　OCR and Translation

　Task Distribution and Workers

　Conclusion

　Task Queues at Lanyrd.com

　Python's Role at Lanyrd

　Making the Task Queue Performant

　Reporting, Monitoring, Debugging, and Deployment

　Advice to a Fellow Developer

　Index