Introduction to Julia Programming Language

June 18, 2015

Where & When

Advanced Manufacturing and Design Centre - Room 206. Thursday 18th June from 3:30-5:30pm.

Introduction to Julia Programming Language

Interested in learning how to code in [Julia]( This week Alex Codoreanu will be leading a 1.5 - 2 hour tutorial/workshop on Julia. No previous experience required. We’ll be setting up laptops at the start of the session and installing the Light Table code editor (optional). If you’ve never heard of Julia but what to see what it’s all about please come along. Those wishing to eavesdrop while working on other projects are welcome too.

The Julia web interface is Jupyter, the same notebook web interface that is used for Python. Researchers wishing to spend more time working with the notebook for Python will probably find this useful. Julia is an interactive language that works with Python and R, so researchers using those languages will definitely get something out of this.

Don’t forget to bring your laptop! All operating systems welcome.

The schedule:

*Note about istallation: There are a couple errors in the cheatsheet and talk. Remeber Juila is case sensitive:

Session resources

PDF talks and Julia example files can be downloaded from the SHW GitHub repository – the julia/ folder

Alex Codoreanu

Alex is a PhD student from the Centre for Astrophysics and Supercomputing. His research focusses on understanding the absorption profiles observed in the spectra of quasars, specifically looking at the relative strengths of different ionization states of Carbon, Oxygen, Silicon and other metals.

About Julia

Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library. Julia’s Base library, largely written in Julia itself, also integrates mature, best-of-breed open source C and Fortran libraries for linear algebra, random number generation, signal processing, and string processing.

Julia is designed for parallelism and cloud computing. It provides a number of key building blocks for distributed computation, making it flexible enough to support a number of styles of parallelism, and allowing users to add more. Like Python, users can lanch the web-based interactive Jupyter Notebook (formerly IJulia Notebook), using Gadfly.

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