The Julia programming language is one of the fastest growing languages in the world. It’s a multi-paradigm language with concepts from functional and imperative languages. As data science and machine learning become more popular, Julia’s blend of high performance and general purpose features have rapidly driven its adoption by such companies as Apple and Disney. With Groxio as a guide, you can explore with us and find out why.
Throughout the Julia language, you can see powerful features supporting productivity, performance, and friendly features for scientists. Advanced concurrency features allow a higher level of parallelism than most other technical languages support. A rich type system provides the extra performance, documentation, and protection when you want it. Multiple dispatch allows the high levels of reuse that object oriented languages promised but never seemed to deliver.
What really sets Julia apart is the libraries. With high-performance libraries available for everything from machine learning to data manipulation, the Julia language shines where many other functional languages fail. The science-friendly abstractions and language features in Julia make it easy to represent math problems with functions.
Until the last few years, technical computing has always embraced performance as an overriding concern. Data scientists liked the way Python worked, but it wasn’t fast enough. In 2009, Jeff Bezanson, Stefan Karpinski, Viral B. Shah, and Alan Edelman started work on Julia to bridge the gap between productivity and performance in technical computing languages. While MIT was a great place to form the new language, a public university is not always the best steward.
In 2015, the founding team brought in a few more employees, and together founded Julia Computing. Under their careful stewardship, the company has cared for the language, adding a conference, several major clients, and a regular string of new language releases with new features. Julia is now the 28th most popular language in the world.