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.

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Table of Contents (19 chapters)
Title Page
Title Page
Copyright and Credits
Copyright and Credits
Julia High Performance Second Edition
Dedication
Dedication
About Packt
About Packt
Why subscribe?
Foreword
Foreword
Contributors
Contributors
About the author
About the reviewers
Packt is searching for authors like you
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Julia is Fast
Julia is Fast
Julia – fast and dynamic
Designed for speed
How fast can Julia be?
Summary
2
Analyzing Performance
Analyzing Performance
Timing Julia functions
The Julia profiler
Analyzing memory allocation
Statistically accurate benchmarking
Summary
3
Types, Type Inference, and Stability
Types, Type Inference, and Stability
The Julia type system
Type-stability
Kernel methods and function barriers
Types in storage locations
Summary
4
Making Fast Function Calls
Making Fast Function Calls
Using globals
Inlining
Using macros for performance
Generated functions
Summary
5
Fast Numbers
Fast Numbers
Numbers in Julia, their layout, and storage
Trading performance for accuracy
Subnormal numbers
Summary
6
Using Arrays
Using Arrays
Array internals in Julia
Bounds checking
Allocations and in-place operations
Broadcasting
Array views
SIMD parallelization (AVX2, AVX512)
Specialized array types
Yeppp!
Writing generic library functions with arrays
Summary
7
Accelerating Code with the GPU
Accelerating Code with the GPU
Technical requirements
Getting started with GPUs
CUDA and Julia
CuArrays
Deep learning on the GPU
ArrayFire
Summary 
8
Concurrent Programming with Tasks
Concurrent Programming with Tasks
Tasks
Using tasks
Communicating between tasks
High-performance I/O
Summary
9
Threads
Threads
Threads
Thread safety and synchronization primitives
Threaded libraries
The future of threading
Summary
10
Distributed Computing with Julia
Distributed Computing with Julia
Creating Julia clusters
Programming parallel tasks
Shared arrays
Summary
Licences
Licences
Other Books You May Enjoy
Other Books You May Enjoy
Leave a review - let other readers know what you think

Other Books You May Enjoy

If you enjoyed this book, you may be interested in these other books by Packt:

Julia Programming Projects
Adrian Salceanu

ISBN: 978-1-78829-274-0

  • Leverage Julia's strengths, its top packages, and main IDE options
  • Analyze and manipulate datasets using Julia and DataFrames
  • Write complex code while building real-life Julia applications
  • Develop and run a web app using Julia and the HTTP package
  • Build a recommender system using supervised machine learning
  • Perform exploratory data analysis
  • Apply unsupervised machine learning algorithms
  • Perform time series data analysis, visualization, and forecasting

Julia 1.0 Programming - Second Edition
Ivo Balbaert

ISBN: 978-1-78899-909-0

  • Set up your Julia environment to achieve high productivity
  • Create your own types to extend the built-in type system
  • Visualize your data in Julia with plotting packages
  • Explore the use of built-in macros for testing and debugging, among other uses
  • Apply Julia to tackle problems concurrently
  • Integrate Julia with other languages such as C, Python, and MATLAB
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