Book Image

Julia 1.0 High Performance - Second Edition

By : Avik Sengupta
Book Image

Julia 1.0 High Performance - Second Edition

By: Avik Sengupta

Overview of this book

Julia is a high-level, high-performance dynamic programming language for numerical computing. If you want to understand how to avoid bottlenecks and design your programs for the highest possible performance, then this book is for you. The book starts with how Julia uses type information to achieve its performance goals, and how to use multiple dispatches to help the compiler emit high-performance machine code. After that, you will learn how to analyze Julia programs and identify issues with time and memory consumption. We teach you how to use Julia's typing facilities accurately to write high-performance code and describe how the Julia compiler uses type information to create fast machine code. Moving ahead, you'll master design constraints and learn how to use the power of the GPU in your Julia code and compile Julia code directly to the GPU. Then, you'll learn how tasks and asynchronous IO help you create responsive programs and how to use shared memory multithreading in Julia. Toward the end, you will get a flavor of Julia's distributed computing capabilities and how to run Julia programs on a large distributed cluster. By the end of this book, you will have the ability to build large-scale, high-performance Julia applications, design systems with a focus on speed, and improve the performance of existing programs.
Table of Contents (19 chapters)
Title Page
Dedication
Foreword
Licences

About the author

Avik Sengupta is the vice president of engineering at Julia Computing, a contributor to open source Julia, and the maintainer of several Julia packages. Avik is the co-founder of two start-ups in the financial services and AI sectors, and is a creator of large, complex trading systems for the world's leading investment banks. Prior to Julia Computing, Avik was co-founder and CTO at AlgoCircle and at Itellix, director at Lab49, and head of algorithmic solutions at Decimal Point Analytics. Avik earned his MS in computational finance at Carnegie Mellon and MBA Finance at the Indian Institute of Management in Bangalore. 

This book wouldn't exist, and my life would have been very different, if, almost over a decade ago, four people across the world did not imagine that scientific computing needed a new language. Thank you, Jeff, Alan, Viral, and Stefan, for launching a thousand ships. 

My colleagues at Julia Computing have been a source of immense support. Among others, Keno Fischer, Matt Bauman, Kristoffer Carlson, and Tanmay Mohapatra have helped clear my misconceptions, and have provided code and ideas that have seeped into this book. I'm also grateful to my reviewers, who've painstakingly provided detailed feedback on this book. All of them have made this book immeasurably better, but the responsibility for any errors, of course, remains my own. 

And finally, a word of thanks to the entire Julia community. The depth and breadth of knowledge and skill I have encountered are exceptional. Over the past few years, I've learned so much—things that I should have known, and things that I never thought I could know. Thank you for sharing your knowledge so generously.