Book Image

Python High Performance Programming

By : Dr. Gabriele Lanaro
Book Image

Python High Performance Programming

By: Dr. Gabriele Lanaro

Overview of this book

<p>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.</p> <p>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.</p> <p>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.</p> <p>This is the perfect guide to help you achieve the best possible performance in your Python applications.</p>
Table of Contents (11 chapters)

Summary


Parallel processing is an effective way to increase the speed of your programs or to handle large amounts of data. Embarassingly parallel problems are excellent candidates for parallelization and lead to a straightforward implementation and optimal scaling.

In this chapter, we illustrated the basics of parallel programming in Python. We learned how to use multiprocessing to easily parallelize programs with the tools already included in Python. Another more powerful tool for parallel processing is IPython parallel. This package allows you to interactively prototype parallel programs and manage a network of computing nodes effectively. Finally, we explored the easy-to-use multithreading capabilities of Cython and OpenMP.

During the course of this book, we learned the most effective techniques to design, benchmark, profile, and optimize Python applications. NumPy can be used to elegantly rewrite Python loops, and if it is not enough, you can use Cython to generate efficient C code. At...