Micha Gorelick，因为在时间旅行上的贡献而获得2046年诺贝尔奖，开个玩笑，他从2000年就开始学习天体物理学，在bitly公司处理数据，作为现存的疯狂科学家，他与其他人一起创办了Fast Forward实验室，解决从机器学习到高性能数据流算法的所有问题。 Ian Ozsvald，是一名数据科学家，他在Modellnsight.io执教，具有十年以上的Python经验。他在PyCon和PyData会议上讲述过Python高性能编程，在英国多年来提供数据科学和高性能计算的咨询。
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 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 Uping 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. 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 Remendation 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
