高性能Python
高性能Python封面图

高性能Python

(美) 戈雷利克 (Gorelick.M.) , (英) 欧日沃尔德 (Ozsvald,I.) , 著

出版社:东南大学出版社

年代:2014

定价:78.0

书籍简介:

你的Python代码也许执行正确,但是你需要更快速的运行。通过探讨隐藏在设计备选方案中的基础理论,这本实践指南将帮助你更深入的理解Python的实现。你将了解如何定位性能瓶颈,从而显著提升高数据流量程序中的代码执行效率。你该如何利用多核架构和集群?或者你该如何搭建一个可以自由伸缩而不会影响可靠性的系统?有经验的Python程序员将会了解到这类问题的具体解决方案,以及来自于各个公司的如何把Python高性能编程用于社交媒体分析,产品机器学习和其它场景中去的曲折故事。

书籍目录:

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

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

内容摘要:

你的Python代码也许运行正确,但是你需要运行得更快速。通过探讨隐藏在设计备选方案中的基础理论,《高性能Python(影印版)》将帮助你更深入地理解Python的实现。你将了解如何定位性能瓶颈,从而显著提升高数据流量程序中的代码执行效率。
  你该如何利用多核架构和集群?或者你该如何搭建一个可以自由伸缩而不会影响可靠性的系统?有经验的Python程序员将会学习到这类问题的具体解决方案,以及来自于各个公司的如何把高性能Python用于社交媒体分析、产品机器学习和其他场景中去的曲折故事。

书籍规格:

书籍详细信息
书名高性能Python站内查询相似图书
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出版地南京出版单位东南大学出版社
版次影印本印次1
定价(元)78.0语种英文
尺寸24 × 17装帧平装
页数印数

书籍信息归属:

高性能Python是东南大学出版社于2015.2出版的中图分类号为 TP311.56 的主题关于 软件工具-程序设计-英文 的书籍。