Co-dfns VS Nim

And if anyone wants an absolute masterclass in tacit programming, have a look at Aaron's Co-dfns compiler. The README has extensive reference material. https://github.com/Co-dfns/Co-dfns/

I don't put a lot of stock in the "write-only" accusation. I think it's mostly used by those who don't know APL because, first, it's clever, and second, they can't read the code. However, if I remember I implemented something in J 10 years ago, I will definitely dig out the code because that's the fastest way by far for me to remember how it works.

This project specifically looks to be done in a flat array style similar to Co-dfns[0]. It's not a very common way to use APL. However, I've maintained an array-based compiler [1] for several years, and don't find that reading is a particular difficulty. Debugging is significantly easier than a scalar compiler, because the computation works on arrays drawn from the entire source code, and it's easy to inspect these and figure out what doesn't match expectations. I wrote most of [2] using a more traditional compiler architecture and it's easier to write and extend but feels about the same for reading and small tweaks. See also my review [3] of the denser compiler and precursor Co-dfns.

As for being read by others, short snippets are definitely fine. Taking some from the last week or so in the APL Farm, {⍵÷⍨+/|-/¯9 ¯11+.○?2⍵2⍴0} and {(⍸⍣¯1+\⎕IO,⍺)⊂[⎕IO]⍵} seemed to be easily understood. Forum links at [4]; the APL Orchard is viewable without signup and tends to have a lot of code discussion. There are APL codebases with many programmers, but they tend to be very verbose with long names. Something like the YAML parser here with no comments and single-letter names would be hard to get into. I can recognize, say, that c⌿¨⍨←(∨⍀∧∨⍀U⊖)∘(~⊢∊LF⍪WS⍨)¨c trims leading and trailing whitespace from each string in a few seconds, but in other places there are a lot of magic numbers so I get the "what" but not the "why". Eh, as I look over it things are starting to make sense, could probably get through this in an hour or so. But a lot of APLers don't have experience with the patterns used here.

[0] https://github.com/Co-dfns/Co-dfns

[1] https://github.com/mlochbaum/BQN/blob/master/src/c.bqn

[2] https://github.com/mlochbaum/Singeli/blob/master/singeli.bqn

[3] https://mlochbaum.github.io/BQN/implementation/codfns.html

[4] https://aplwiki.com/wiki/Chat_rooms_and_forums

This always seemed like a very interesting project; we need to get to the point where, if things can run in parallel, they must run in parallel to make software more efficient on modern cpu/gpu.

It won't attract funds, I guess, but it would be far more trivial to make this work with an APL or a Lisp/Scheme. There already is great research for APL[0] and looking at the syntax of HVM-core it seems it is rather easy to knock up a CL DSL. If only there were more hours in a day.

[0] https://github.com/Co-dfns/Co-dfns

There are many styles of APL, not just due to its long history, but also because APL is somewhat agnostic to architecture paradigms. You can see heavily imperative code with explicit branching all over the place, strongly functional-style with lots of small functions, even object-oriented style.

However, given the aesthetic that you express, I think you might like https://github.com/Co-dfns/Co-dfns/. This is hands-down my favorite kind of APL, in which the data flow literally follows the linear code flow.

Which is, as far as I know, unused. (Similarly the gpu compiler.)

You might be familiar with iKe (grahics), SpecialK (GLSL) and Co-dfns. Also, I am working on bastardized APL for GPU – Fluent. Fluent 1 had backend implemented through Apple Metal Performance Shaders Graph and Fluent 2 has TensorFlowJS backend for now. I care more about having auto differentiation in the lang than running on GPU and do graphics, to be honest.

Not that I am aware of. I think the closest project is co-dfns[1] which is being developed by Aaron Hsu (he did a presentation as well). It aims to compile a subset of APL so that it can be executed on GPUs for instance, possibly with other backends. I imagine an XLA backend could be possible there.

[1] https://github.com/Co-dfns/Co-dfns

Aaron hsu did his dissertation on this topic (compiler, thesis), at indiana university in the us.

22. Nim - $80,000

I think the right answer to your question would be NimLang[0]. In reality, if you're seeking to use this in any enterprise context, you'd most likely want to select the subset of C++ that makes sense for you or just use C#.

[0]https://nim-lang.org/

I don't think it's a rust-inspired language, but since it has strong typing and compiles to javascript, did you give a look at nim [0] ?

For what it takes, I find the language very expressive without the verbosity in rust that reminds me java. And it is also very flexible.

[0] : https://nim-lang.org/

I see a valid cert for https://nim-lang.org/

FYI, on the front page, https://nim-lang.org, in large type you have this:

> Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula.

You better off with using a compiled language.

If you interested in a language that's compiled, fast, but as easy and pleasant as Python - I'd recommend you take a look at [Nim](https://nim-lang.org).

And to prove what Nim's capable of - here's a cool repo with 100+ cli apps someone wrote in Nim: [c-blake/bu](https://github.com/c-blake/bu)

Chapel has at least several full-time developers at Cray/HPE and (I think) the US national labs, and has had some for almost two decades. That's much more than $100k.

Chapel is also just one of many other projects broadly interested in developing new programming languages for "high performance" programming. Out of that large field, Chapel is not especially related to the specific ideas or design goals of Mojo. Much more related are things like Codon (https://exaloop.io), and the metaprogramming models in Terra (https://terralang.org), Nim (https://nim-lang.org), and Zig (https://ziglang.org).

But Chapel is great! It has a lot of good ideas, especially for distributed-memory programming, which is its historical focus. It is more related to Legion (https://legion.stanford.edu, https://regent-lang.org), parallel & distributed Fortran, ZPL, etc.