Chapter 1. The Groundwork – Julia's Environment
Julia is a fairly young programming language. In 2009, three developers (Stefan Karpinski, Jeff Bezanson, and Viral Shah) at MIT in the Applied Computing group under the supervision of Prof. Alan Edelman started working on a project that lead to Julia. In February 2012, Julia was presented publicly and became open source. The source code is available on GitHub (https://github.com/JuliaLang/julia). The source of the registered packages can also be found on GitHub. Currently, all four of the initial creators, along with developers from around the world, actively contribute to Julia.
Note
The current release is 0.4 and is still away from its 1.0 release candidate.
Based on solid principles, its popularity is steadily increasing in the field of scientific computing, data science, and high-performance computing.
This chapter will guide you through the download and installation of all the necessary components of Julia. This chapter covers the following topics:
- How is Julia different?
- Setting up Julia's environment.
- Using Julia's shell and REPL.
- Using Jupyter notebooks
- Package management
- Parallel computation
- Multiple dispatch
- Language interoperability
Traditionally, the scientific community has used slower dynamic languages to build their applications, although they have required the highest computing performance. Domain experts who had experience with programming, but were not generally seasoned developers, always preferred dynamic languages over statically typed languages.
