Micha Gorelick, Ian Ozsvald - High Perfomance Python [2014, ePub, ENG]

Chapter 1 Understanding Performant Python
The Fundamental Computer System
Putting the Fundamental Elements Together
So Why Use Python?
Chapter 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
Unit Testing During Optimization to Maintain Correctness
Strategies to Profile Your Code Successfully
Wrap-Up
Chapter 3 Lists and Tuples
A More Efficient Search
Lists Versus Tuples
Wrap-Up
Chapter 4 Dictionaries and Sets
How Do Dictionaries and Sets Work?
Dictionaries and Namespaces
Wrap-Up
Chapter 5 Iterators and Generators
Iterators for Infinite Series
Lazy Generator Evaluation
Wrap-Up
Chapter 6 Matrix and Vector Computation
Introduction to the Problem
Aren’t Python Lists Good Enough?
Memory Fragmentation
Applying numpy to the Diffusion Problem
numexpr: Making In-Place Operations Faster and Easier
A Cautionary Tale: Verify “Optimizations” (scipy)
Wrap-Up
Chapter 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
Cython
Shed Skin
Cython and numpy
Numba
Pythran
PyPy
When to Use Each Technology
Foreign Function Interfaces
Wrap-Up
Chapter 8 Concurrency
Introduction to Asynchronous Programming
Serial Crawler
gevent
tornado
AsyncIO
Database Example
Wrap-Up
Chapter 9 The multiprocessing Module
An Overview of the Multiprocessing Module
Estimating Pi Using the Monte Carlo Method
Estimating Pi Using Processes and Threads
Finding Prime Numbers
Verifying Primes Using Interprocess Communication
Sharing numpy Data with multiprocessing
Synchronizing File and Variable Access
Wrap-Up
Chapter 10 Clusters and Job Queues
Benefits of Clustering
Drawbacks of Clustering
Common Cluster Designs
How to Start a Clustered Solution
Ways to Avoid Pain When Using Clusters
Three Clustering Solutions
NSQ for Robust Production Clustering
Other Clustering Tools to Look At
Wrap-Up
Chapter 11 Using Less RAM
Objects for Primitives Are Expensive
Understanding the RAM Used in a Collection
Bytes Versus Unicode
Efficiently Storing Lots of Text in RAM
Tips for Using Less RAM
Probabilistic Data Structures
Chapter 12 Lessons from the Field
Adaptive Lab’s Social Media Analytics (SoMA)
Making Deep Learning Fly with RadimRehurek.com
Large-Scale Productionized Machine Learning at Lyst.com
Large-Scale Social Media Analysis at Smesh
PyPy for Successful Web and Data Processing Systems
Task Queues at Lanyrd.com