Three years of using Julia

I started using the Julia programming language in 2014, and by 2015, it represented about 75% of my programming time/output. These have been interesting years, because the language changed a lot, but also because I found a tool that makes me very productive.

My two favourite pieces of cookware are a Victorinox chef knife, and a Lodge cast-iron skillet. I use the chef knife to do things it’s not really meant to do (like garlic purée), or things that should probably be done with a paring knife. But I’m efficient with it, and it makes me happy. The same goes for the cast-iron. Some things should probably be done in non-stick, but I don’t care. I love doing them this way, and they turn out alright in the end.

Programming languages are the same: they are tools. A good tool is one that makes you productive, happy, and allow you to deliver a usable product. For me, Julia allowed all of that, and this is in large part because of the things I do often (write custom code to do various numerical things), the things I do somewhat often (differential equations, embarrassingly parallel problems), and the things I don’t do very often at all (statistics).

The one area where Julia shines is that it eliminates the need for intermediate steps when preparing code. For one of my first large simulation projects, I wrote initial proof of concept code in R. Then I moved it to python, and after spending some time identifying bottlenecks, I re-wrote these parts in C. I don’t do this anymore: I use the same language for proof of concept, prototyping, and production. And because I spent a bit of time reading the performance tips section of the manual, the code for the three steps is often very similar. This is a huge time saver, and it decreases the cognitive load associated with switching languages.

One thing I have noticed over the last two years is that my use of supercomputer time decreased. Because the parallel computing abilities out of the box are really strong, most of what I need can be done on my 32-cores desktop. In a recent instance, porting code from python to Julia decreased the time of execution by two orders of magnitudes: investing the time to make this run on a cluster, and waiting for a spot in the queue, was not worth it anymore.

And now is the right time to start learning the language. The Discourse community is very quick at answering, and very welcoming of newcomers. The v0.7 will be stable (or very close to), so there will be no more deprecation when v1.0 is released. The ecosystem of packages is starting to diversify, and JuliaDB is a very good solution for data crunching.