Julia is a relatively new, free and open source scientific programming language that has come out of MIT. I first played with it in 2012, back in the days when it didn’t even have release numbers — just github hashes and it has come a long way since then! In my mind, I think of it as what a language would look like if your two primary design parameters were ‘Easy to use for newbies’ and ‘Maximally JITable‘ — this is almost certainly a gross oversimplification but it doesn’t seem to offend some of the people who helped create the language. Another way to think about it is ‘As easy to write as Python or MATLAB but with speed on par with C or Fortran’.
I attended Julia’s annual conference, JuliaCon, last week along with around 350 other delegates from almost every corner of the Research Computing world. While there, I gave a talk on ‘The Rise of the Research Software Engineer’. This was the first time I’d ever had one of my talks recorded and you can see the result below
All of the conference talks are available at https://www.youtube.com/user/JuliaLanguage/videos. If you’d like to get a flavour of what Julia can do for your computational research, a couple of the JuliaCon 2018 tutorials I’d recommend are below
An Introduction to Julia
Machine learning with Julia
JuliaCon 2018 marked an important milestone for the language when version 1.0 was released so now is a fantastic time to try out the language for the first time. You can install it on your own machines from https://julialang.org/downloads/ and we’ve also installed it on ARC3. You can make it available to your ARC3 session using the following module command
module load julia/1.0.0