Julia: High Performance Programming
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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.
What You Will LearnSet up your Julia environment to achieve the highest productivitySolve your tasks in a high-level dynamic language and use types for your data only when neededApply Julia to tackle problems concurrently and in a distributed environmentGet a sense of the possibilities and limitations of Julia's performanceUse Julia arrays to write high performance codeBuild a data science project through the entire cycle of ETL, analytics, and data visualizationDisplay graphics and visualizations to carry out modeling and simulation in JuliaDevelop your own packages and contribute to the Julia CommunityIn DetailIn this learning path, you will learn to use an interesting and dynamic programming language—Julia! You will get a chance to tackle your numerical and data problems with Julia. You'll begin the journey by setting up a running Julia platform before exploring its various built-in types. We'll then move on to the various functions and constructs in Julia. We'll walk through the two important collection types—arrays and matrices in Julia.
You will dive into how Julia uses type information to achieve its performance goals, and how to use multiple dispatch to help the compiler emit high performance machine code. You will see how Julia's design makes code fast, and you'll see its distributed computing capabilities.
By the end of this learning path, you will see how data works using simple statistics and analytics, and you'll discover its high and dynamic performance—its real strength, which makes it particularly useful in highly intensive computing tasks.
This learning path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:
Getting Started with Julia by Ivo BalvaertJulia High Performance by Avik SenguptaMastering Julia by Malcolm SherringtonStyle and approachThis hands-on manual will give you great explanations of the important concepts related to Julia programming.
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Ivo Balbaert is currently a lecturer in (web) programming and databases at CVO Antwerpen (www.cvoantwerpen.be), a community college in Belgium. He received a PhD degree in applied physics from the University of Antwerp in 1986. He worked for 20 years in the software industry as a developer and consultant in several companies, and for 10 years as a project manager at the University Hospital of Antwerp. From 2000 onward, he switched to partly teaching and developing software (KHM Mechelen, CVO Antwerp). He also wrote an introductory book in Dutch about developing in Ruby and Rails, Programmeren met Ruby en Rails, Van Duuren Media. In 2012, he authored a book on the Go programming language, The Way To Go, iUniverse. In 2013, in collaboration with Dzenan Ridzanovic, he authored Learning Dart and Dart Cookbook, both by Packt Publishing.
Avik Sengupta has worked on risk and trading systems in investment banking for many years, mostly using Java interspersed with snippets of the exotic R and K languages. This experience left him wondering whether there were better things out there. Avik's quest came to a happy conclusion with the appearance of Julia in 2012. He has been happily coding in Julia and contributing to it ever since.
Malcolm Sherrington has been working in computing for over 35 years. He holds degrees in mathematics, chemistry, and engineering and has given lectures at two different universities in the UK as well as worked in the aerospace and healthcare industries. Currently, he is running his own company in the finance sector, with specific interests in High Performance Computing and applications of GPUs and parallelism. Always hands-on, Malcolm started programming scientific problems in Fortran and C, progressing through Ada and Common Lisp, and recently became involved with data processing and analytics in Perl, Python, and R. Malcolm is the organizer of the London Julia User Group. In addition, he is a co-organizer of the UK High Performance Computing and the financial engineers and Quant London meetup groups.
