How MIT's Julia programming language conquered the world
How an MIT research project became a global programming language
In 2009, MIT researchers frustrated by slow, rigid scientific programming languages began building Julia, a language that combines Python's ease with C's speed. Now with over 1 million users, Julia powers everything from black hole imaging to Moderna's COVID-19 vaccine. The team behind it, now the company JuliaHub, recently launched Dyad 3.0, an AI platform that can design entire aircraft from uploaded documents, enforcing physical laws to accelerate engineering by orders of magnitude.
Researchers come up to me and say, 'I tell my supervisor I'm using Julia because it's fast, but don't tell them I'm using Julia because it's really fun.'
- eigenspace
Julia is such a great language, it's what really made me care about end enjoy programming. I think it's the modern language which has taken the most lessons from lisps, and has come up with some great ideas of its own too.
There's lots to like, but I think the thing I love most about it and find it so interesting is that it's almost uniquely good at taking a piece of code and transforming it's meaning in various ways, and has so many tools for doing so. There's
* Multiple dispatch allowing very flexible writing of generic code, and multiple dispatch isn't some tacked on, opt-in extra. Every function in the language is overloadable, and there's no performance penalty for using multiple dispatch
* Parametric typing allows for a huge amount of abstraction over common 'base' types
* Lispy macros let you do metaprogramming that changes the meaning of a piece of syntax
* Generated functions let you intervene at compile time and lets you essentially take over the compilation pipeline and customize the code generation for any given input type signature
* The abstract interpreter interface which lets one essentially take over the compiler and customize your code generation and analysis passes to your heart's content. This is used for instance to support GPUs and automatic differentiation as package offerings.
- prof-dr-ir
Julia is fantastic if you do numerical work and want to write out your inner loops explicitly without sacrificing too much performance, either for pedagogical reasons or because you want to fiddle with the algorithm.
It is a lot of fun to start from an empty file, add maybe an import LinearAlgebra, and develop things like a convolutional neural network or a Markov-chain Monte Carlo algorithm completely from scratch. And then it is very rewarding to have such a program be fast enough (looking at Python here) to train on the MNIST dataset or find reasonable estimates for critical exponents.
I am not sure there are languages better suited than Julia for these kind of things.
- radiator
Do you think the criticism in the article "Why I no longer recommend Julia" about correctness is still valid? * there are too many correctness and composability bugs throughout the ecosystem to justify using it in just about any context where correctness matters *
- maxall4
I really want to like Julia. It has a nice type system, a good ecosystem, reasonable syntax, and it’s far faster than Python. But there are several issues with its DX that prevent me from using it: the most severe of which being the complete lack of a cache for the JIT (or JIT like system), inducing multi second compile times for scripts that run in <100ms. There are some external packages that try to solve this issue, but they are far from first-party quality, and, in my experience, are quite buggy.
(I last tried Julia a few years ago; perhaps this has been improved since?)
- drhagen
When I was a grad student at MIT, my desk was across the hall from the Julia people. We even shared a lunch table. Julia is most famous for its optimizing compiler, but many people may not notice the careful work that went into designing the language itself. Matlab this was not! I remember one long conversation where one of them was teaching me about the different types of normalization of unicode. They had to pick one to canonicalize unicode variable names (more useful for a mathy programming language), and they were carefully considering the consequences of the options.