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

Summary

In this chapter, we covered the performance characteristics in Julia of the most important data structure in scientific computing—the array. We discussed why Julia's design enables extremely fast array operations and how to get the best performance in our code when operating on arrays. This brings us to the end of our journey of creating the fastest possible code in Julia. Using all the tips discussed until now, the performance of your code should approach that of well-written C.

Sometimes, however, this isn't enough, and we want higher performance; our data may be larger or our computations intensive. In this case, the only option is to parallelize our processing using multiple CPUs and systems. In the next chapter, we will take a brief look at the features that Julia provides to write parallel systems easily.