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|MIT License||MIT License|
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news.ycombinator.com | 2021-06-10
From Competitive Programming to APL and Array Programming
reddit.com/r/programming | 2021-06-02
dev.to | 2021-10-28
Julia is by far one of the most popular and fastest growing language in the coding community ,it is free and open source and is developed by MIT. It is expected to surpass all the current languages in the data science domain in next few years because it is more faster, dynamic and efficient. It also has foreign function interfaces for languages such as C, C++, Python, Java. Julia is organising a hackathon this month do participate in it and contribute to the community here's the link https://julialang.org/
How I keep my sanity through a car hunt
reddit.com/r/Asphalt9 | 2021-10-26
Just joking, I use Julia XD
Why Lisp? (2015)
news.ycombinator.com | 2021-10-26
there was dylan before julia, so julia might just be reinventing dylan :) but that's not what's interesting
the julia project is in fact very interesting to me and has a great team developing its ecosystem and i work with it alongside python for numerical work. however one key drawback (compared to common lisp) for me is that it is not self-compiled. it is hosted on llvm and over 30% of its repository is in another language (mainly C and C++). but i am saying this only in comparison to common lisp. other languages are not different to this, and are much worse. as far as scientific computing is concerned, i would work with julia over python any day
Arrays start from bony
reddit.com/r/ProgrammerHumor | 2021-10-25
There's Julia, and with its high-level syntax and performance comparable to C, I'd say it's not dumb, just different.
To Learn a New Language, Read Its Standard Library
news.ycombinator.com | 2021-10-24
When researching and developing new algorithms to be used in the real-world production environment, what is your workflow and how do you usually do it? Do I have to prototype in Python, and then rewrite all code in C++/Rust?
reddit.com/r/Python | 2021-10-23
I am using Julia https://julialang.org/ for this purpose, fast prototyping and similar run time performance as Rust/C.reddit.com/r/Python | 2021-10-23
What kind of solutions do you prefer and what area of maths do you study?
reddit.com/r/mathmemes | 2021-10-19
The Go+ language for engineering, STEM education, and data science
news.ycombinator.com | 2021-10-17
Recursion absolutely necessary for distributed computing?
reddit.com/r/Julia | 2021-10-15
Then, if everything is pure functions, instead of iteratively mutating an array, you can recursively do calls that make new arrays that change one element at a time. But wait, doesn't that sound very inefficient compared to mutation? Well yes it does! It would satisfy the purity argument to allow a compiler to know how to auto-parallelize, but it would be making so many temporary arrays that it would likely be slower than a good explicit loop. For this reason you need compiler optimizations which would remove the intermediate arrays and transform it under the hood to mutating code (see https://github.com/JuliaLang/julia/pull/42465 as an example of this in the Julia compiler). This is one optimization that is needed, another is the tail-call elimination that I mentioned earlier, etc. If you have all pure functions, and if all of these optimizations are perfect, then you can match the serial code performance of C/Fortran. But that is a big if, which is why you don't see successful BLAS's written in say Haskell (GHC is a good compiler but it's hard to make this perfect).
What are some alternatives?
rust-numpy - PyO3-based Rust binding of NumPy C-API
NetworkX - Network Analysis in Python
Numba - NumPy aware dynamic Python compiler using LLVM
Lua - Lua is a powerful, efficient, lightweight, embeddable scripting language. It supports procedural programming, object-oriented programming, functional programming, data-driven programming, and data description.
Dagger.jl - A framework for out-of-core and parallel execution
duckdf - 🦆 SQL for R dataframes, with ducks
py2many - Python to CLike languages transpiler
DFTK.jl - Density-functional toolkit
JET.jl - scratch: experimental code analyzer for Julia, no need for additional type annotations
femtolisp - a lightweight, robust, scheme-like lisp implementation
PackageCompiler.jl - Compile your Julia Package
awesome-lisp-companies - Awesome Lisp Companies