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Ivo Balbaert, Avik Sengupta, Malcolm Sherrington Julia: High Performance Programming Learning Path Leverage the power of Julia to design and develop high performing programs Julia: High Performance Programming Leverage the power of Julia to design and develop high performing programs A course in three modules BIRMINGHAM - MUMBAI Julia: High Performance Programming Copyright © 2016 Packt Publishing All rights reserved. No part of this course may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews. Every effort has been made in the preparation of this course to ensure the accuracy of the information presented. However, the information contained in this course is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this course. Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this course by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information. Published on: November 2016 Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK. ISBN 978-1-78712-570-4 www.packtpub.com Credits Authors Content Development Editor Ivo Balbaert Priyanka Mehta Avik Sengupta Malcolm Sherrington Graphics Disha Haria Reviewers Pascal Bugnion Production Coordinator Aparna Bhagat Michael Otte Dustin Stansbury Zhuo QL Gururaghav Gopal Dan Wlasiuk Preface Julia is a relatively young programming language. The initial design work on the Julia project began at MIT in August 2009, and by February 2012, it became open source. It is largely the work of three developers Stefan Karpinski, Jeff Bezanson, and Viral Shah. These three, together with Alan Edelman, still remain actively committed to Julia and MIT currently hosts a variety of courses in Julia, manyof which are available over the Internet. Initially, Julia was envisaged by the designers as a scientific language sufficiently rapid to make the necessity of modeling in an interactive language and subsequently having to redevelop in a compiled language, such as C or Fortran. At that time the major scientific languages were propriety ones such as MATLAB and Mathematica, and were relatively slow. There were clones of these languages in the open source domain, such as GNU Octave and Scilab, but these were even slower. When it launched, the community saw Julia as a replacement for MATLAB, but this is not exactly the case. Although the syntax of Julia is similar to MATLAB, so much so that anyone competent in MATLAB can easily learn Julia, it was not designed as a clone. It is a more feature-rich language with many significant differences that will be discussed in depth later. The period since 2009 has seen the rise of two new computing disciplines: big data/ cloud computing and data science. Big data processing on Hadoop is conventionally seen as the realm of Java programming, since Hadoop runs on the Java virtual machine. It is, of course, possible to process big data by using programming languages other than those that are Java-based and utilize the streaming-jar paradigm, and Julia can be used in a way similar to C++, C#, and Python. [ i ] Preface The emergence of data science heralded the use of programming languages that were simple for analysts with some programming skills but who were not principally programmers. The two languages that stepped up to fill the breach have been R and Python. Both of these are relatively old with their origins back in the 1990s. However, the popularity of these two has seen a rapid growth, ironically from around the time when Julia was introduced to the world. Even so, with such estimated and staid opposition, Julia has excited the scientific programming community and continues to make inroads in this space. The aim of this course is to cover all aspects of Julia that make it appealing to the data scientist. The language is evolving quickly. Binary distributions are available for Linux, Mac OS X, and Linux, but these will lag behind the current sources. So, to do some serious work with Julia, it is important to understand how to obtain and build a running system from source. In addition, there are interactive development environments available for Julia and the course will discuss both the Jupyter and Juno IDEs. What this learning path covers Module 1, Getting Started with Julia, a head start to tackle your numerical and data problems with Julia. Your journey will begin by learning how to set up a running Julia platform before exploring its various built-in types. You will then move on to cover the different functions and constructs in Julia. The module will then walk you through the two important collection types―arrays and matrices. Over the course of the module, you will also be introduced to homoiconicity, the meta-programming concept in Julia. Towards the concluding part of the module, you will also learn how to run external programs. This module will cover all you need to know about Julia to leverage its high speed and efficiency. Module 2, Julia High Performance, will take you on a journey to understand the performance characteristics of your Julia programs, and enables you to utilize the promise of near C levels of performance in Julia. You will learn to analyze and measure the performance of Julia code, understand how to avoid bottlenecks, and design your program for the highest possible performance. In this module, you will also see how Julia uses type information to achieve its performance goals, and how to use multiple dispatch to help the compiler to emit high performance machine code. Numbers and their arrays are obviously the key structures in scientific computing – you will see how Julia's design makes them fast. [ ii ] Preface Module 3, Mastering Julia, you will compare the different ways of working with Julia and explore Julia's key features in-depth by looking at design and build. You will see how data works using simple statistics and analytics, and discover Julia's speed, its real strength, which makes it particularly useful in highly intensive computing tasks and observe how Julia can cooperate with external processes in order to enhance graphics and data visualization. Finally, you will look into meta-programming and learn how it adds great power to the language and establish networking and distributed computing with Julia. What you need for this learning path Developing in Julia can be done under any of the familiar computing operating systems: Linux, OS X, and Windows. To explore the language in depth, the reader may wish to acquire the latest versions and to build from source under Linux. However, to work with the language using a binary distribution on any of the three platforms, the installation is very straightforward and convenient. In addition, Julia now comes pre-packaged with the Juno IDE, which just requires expansion from a compressed (zipped) archive. Who this learning path is for This learning path is for data scientists and for all those who work in technical and scientific computation projects. It will be great for Julia developers who are interested in high-performance technical computing. This learning path assumes that you already have some basic working knowledge of Julia's syntax and high-level dynamic languages such as MATLAB, R, Python, or Ruby. Reader feedback Feedback from our readers is always welcome. Let us know what you think about this course—what you liked or disliked. Reader feedback is important for us as it helps us develop titles that you will really get the most out of. To send us general feedback, simply e-mail [email protected], and mention the title of the course in the subject of your message. If there is a topic that you have expertise in and you are interested in either writing or contributing to any of our product, see our author guide at www.packtpub.com/ authors. [ iii ] Preface Customer support Now that you are the proud owner of a Packt course, we have a number of things to help you to get the most from your purchase. Downloading the example code You can download the example code files for this course from your account at http://www.packtpub.com. If you purchased this course elsewhere, you can visit http://www.packtpub.com/support and register to have the files e-mailed directly to you. You can download the code files by following these steps: 1. Log in or register to our website using your e-mail address and password. 2. Hover the mouse pointer on the SUPPORT tab at the top. 3. Click on Code Downloads & Errata. 4. Enter the name of the course in the Search box. 5. Select the course for which you're looking to download the code files. 6. Choose from the drop-down menu where you purchased this book from. 7. Click on Code Download. You can also download the code files by clicking on the Code Files button on the course's webpage at the Packt Publishing website. This page can be accessed by entering the course's name in the Search box. Please note that you need to be logged into your Packt account. Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of: • WinRAR / 7-Zip for Windows • Zipeg / iZip / UnRarX for Mac • 7-Zip / PeaZip for Linux The code bundle for the course is also hosted on GitHub at https://github.com/ PacktPublishing/Julia-High-Performance-Programming. We also have other code bundles from our rich catalog of books and videos available at https:// github.com/PacktPublishing/. Check them out! [ iv ] Preface Errata Although we have taken every care to ensure the accuracy of our content, mistakes do happen. If you find a mistake in one of our books/courses—maybe a mistake in the text or the code—we would be grateful if you could report this to us. By doing so, you can save other readers from frustration and help us improve subsequent versions of this course. 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Please contact us at [email protected] with a link to the suspected pirated material. We appreciate your help in protecting our authors and our ability to bring you valuable content. Questions If you have a problem with any aspect of this course, you can contact us at [email protected], and we will do our best to address the problem. [ v ]
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