Lanaro - Python High Performance - Second Edition

BIRMINGHAM - MUMBAI

Copyright 2017 Packt Publishing

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First published: December 2013

Second edition: May 2017

Production reference: 2250517

ISBN 978-1-78728-289-6

www.packtpub.com

Dr. Gabriele Lanaro has been conducting research to study the formation and growth of crystals using medium and large-scale computer simulations. In 2017, he obtained his PhD in theoretical chemistry. His interests span machine learning, numerical computing visualization, and web technologies. He has a sheer passion for good software and is the author of the chemlab and chemview open source packages. In 2013, he authored the first edition of the book High Performance Python Programming.

I'd like to acknowledge the support from Packt editors, including Vikas Tiwari. I would also like to thank my girlfriend, Harani, who had to tolerate the way-too-long writing nights, and friends who provided company and support throughout. Also, as always, Id love to thank my parents for giving me the opportunity to pursue my ambitions.

Lastly, I would like to thank Blenz coffee for powering the execution engine of this book through electricity and caffeine.

Will Brennan is a C++/Python developer based in London with previous experience in writing molecular dynamics simulations. He is currently working on high-performance image processing and machine learning applications. You can refer to his repositories at https://github.com/WillBrennan.

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The Python programming language has seen a huge surge in popularity in recent years, thanks to its intuitive, fun syntax, and its vast array of top-quality third-party libraries. Python has been the language of choice for many introductory and advanced university courses as well as for numerically intense fields, such as the sciences and engineering. Its primary applications also lies in machine learning, system scripting, and web applications.

The reference Python interpreter, CPython, is generally regarded as inefficient when compared to lower-level languages, such as C, C++, and Fortran. CPythons poor performance lies in the fact that the program instructions are processed by an interpreter rather than being compiled to efficient machine code. While using an interpreter has several advantages, such as portability and the additional compilation step, it does introduce an extra layer of indirection between the program and the machine, which causes a less efficient execution.

Over the years, many strategies have been developed to overcome CPython's performance shortcomings. This book aims to fill this gap and will teach how to consistently achieve strong performance out of your Python programs.

This book will appeal to a broad audience as it covers both the optimization of numerical and scientific codes as well as strategies to improve the response times of web services and applications.

The book can be read cover-to-cover ; however, chapters are designed to be self-contained so that you can skip to a section of interest if you are already familiar with the previous topics.