High Performance Python, 2nd Edition

Index

Symbols

• %memit, Objects for Primitives Are Expensive, Understanding the RAM Used in a Collection
• %timeit function, Profiling Efficiently, Simple Approaches to Timing—print and a Decorator, Different Approaches, Different Complexity, There’s More Than One (and Possibly a Faster) Way to Do a Job
• -p portability flag, Simple Timing Using the Unix time Command
• / (integer division/float division), Changes from Python 2.7
• 1D diffusion, Introduction to the Problem
• 2D diffusion, Introduction to the Problem
• 32-bit Python, Python 3
• 64-bit Python, Python 3
• >> (jump points), Using the dis Module to Examine CPython Bytecode
• @jit decorator, Numba, Numba, Best Practices and Recommendations, Best Practices and Recommendations
• @profile decorator, No-op @profile Decorator

A

• abs function, Why Does Type Information Help the Code Run Faster?, Adding Some Type Annotations
• Accelerate, Sharing numpy Data with multiprocessing
• Adaptive Lab, Adaptive Lab’s Social Media Analytics (2014)-Advice for Fellow Engineers
• Aho-Corasick trie, High Performance Real-Time String Matching
• aiohttp, aiohttp-Shared CPU–I/O Workload
• Airflow, Asynchronously adding jobs to the Queue, Other Clustering Tools to Look At
• algorithms, Lists and Tuples, Iterators and Generators, K-Minimum Values-K-Minimum Values
• Amazon EC2, Common Cluster Designs, Our Development Methodology, Cluster Design
• Amazon Machine Image, Ways to Avoid Pain When Using Clusters
• Amazon Web Services (AWS), Clusters and Job Queues, Python’s Role at Smesh
• Amazon's Simple Queue Service ...