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Python High Performance Programming Boost the performance of your Python programs using advanced techniques

Python is a programming language with a vibrant community known for its simplicity, code readability, and expressiveness. The massive selection of third party libraries make it suitable for a wide range of applications. This also allows programmers to express concepts in fewer lines of code than would be possible in similar languages. The availability of high quality numerically-focused tools has made Python an excellent choice for high performance computing. The speed of applications comes down to how well the code is written. Poorly written code means poorly performing applications, which means unsatisfied customers.

This book is an example-oriented guide to the techniques used to dramatically improve the performance of your Python programs. It will teach optimization techniques by using pure python tricks, high performance libraries, and the python-C integration. The book will also include a section on how to write and run parallel code.

This book will teach you how to take any program and make it run much faster. You will learn state-of the art techniques by applying them to practical examples. This book will also guide you through different profiling tools which will help you identify performance issues in your program. You will learn how to speed up your numerical code using NumPy and Cython. The book will also introduce you to parallel programming so you can take advantage of modern multi-core processors.

This is the perfect guide to help you achieve the best possible performance in your Python applications

Table of Contents
1. Benchmarking and Profiling
  • Designing your application
  • Writing tests and benchmarks
  • Finding bottlenecks with cProfile
  • Profile line by line with line_profiler
  • Optimizing our code
  • The dis module
  • Profiling memory usage with memory_profiler
  • Performance tuning tips for pure Python code
  • Summary
2. Fast Array Operations with NumPy
  • Getting started with NumPy
  • Rewriting the particle simulator in NumPy
  • Reaching optimal performance with numexpr
  • Summary
3. C Performance with Cython
  • Compiling Cython extensions
  • Adding static types
  • Sharing declarations
  • Working with arrays
  • Particle simulator in Cython
  • Profiling Cython
  • Summary
4. Parallel Processing
  • Introduction to parallel programming
  • The multiprocessing module
  • IPython parallel
  • Parallel Cython with OpenMP
  • Summary

  

You will get a PDF (1MB) file

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