高性能Python（影印版）

　　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?
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　　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，戈雷利克和欧日沃尔德编著的《高性能Python》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。

　　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
^ 收 起　　

　　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，戈雷利克和欧日沃尔德编著的《高性能Python》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。