In this post I am going to try to explain in detail the type-dispatch design which is used in Julian software architectures. It’s modeled after the design of many different packages and Julia Base, and has been discussed in parts elsewhere. This is actually just a blog post translation from my “A Deep Introduction to Julia for Data Science and Scientific Computing” workshop notes. I think it’s an important enough topic to share more broadly.

The tl;dr: Julia is built around types. Software architectures in Julia are built around good use of the type system. This makes it easy to build generic code which works over a large range of types and gets good performance. The result is high-performance code that has many features. In fact, with generic typing, your code may have more features than you know of! The … READ MORE

When most people think of the Julia programming language, they usually think about its speed. Sure, Julia is fast, but what I emphasized in a previous blog post is that what makes Julia tick is not just-in-time compilation, rather it’s specialization. In that post, I talked a lot about how multiple dispatch allows for Julia to automatically specialize functions on the types of the arguments. That micro-specialization is what allows Julia to compile functions which are as fast as those from C/Fortran.

However, there is a form of “macro specialization” that Julia’s design philosophy and multiple dispatch allows one to build libraries for. It allows you to design an algorithm in a very generic form, essentially writing your full package with inputs saying “insert scientific computing package here”, allowing users to specialize the entire overarching algorithm to the specific problem. … READ MORE