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How an MIT research project became the Julia programming language
46 points by theanonymousone
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10 comments
ashton314
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Julia is my go-to when I need a fast language that I can reason about in a functional way. The type system is fine, the pattern matching is pretty darn good, the metaprogramming story could be significantly better but it’s not bad and boy howdy it’s fast. So stinking fast.
maxall4
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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?)
muragekibicho
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Julia uses 1-based indexing. It's competes with R and Matlab for the same set of users. Both R and Julia have their core functions written in C++. Absolutely nothing new.
From my experience, grad students use Julia when their PI thinks a new programming language will help differentiate their next NSF proposal among vast funding requests.
adgjlsfhk1
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> Both R and Julia have their core functions written in C++. Absolutely nothing new.
imo this isn't really a good summary. Julia is one of the 3 languages to have done an exascale HPC run https://arxiv.org/pdf/2309.10292v1 (Fortran and C are the other 2). Some parts of the compiler are written in c++ (the llvm interface), but doing codegen with llvm is much more similar to C/Rust/Fortran than R/Matlab