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

Writing generic library functions with arrays

The suggestions in the previous sections should make your array code fast and high performance. If you are directly writing code to solve your own problems, this should be enough. However, if you are writing library routines that may be called by other programs, you will need to heed additional concerns. Your function may be called with arrays of different kinds and with different dimensions. To write generic code that is fast with all custom types built-in, and for arrays of many dimensions, you need to be careful in how you iterate over the elements of the arrays.

All Julia arrays are subtypes of the AbstractArray type. All abstract arrays must provide facilities for indexation and iteration. However, these can be implemented very differently for different types of arrays. The default array is DenseArray, which stores its elements...