Co-dfns VS april

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.

There are attempts to combine those...

April (Array Programming Re-Imagined in Lisp)

https://github.com/phantomics/april

> operations that apply to the whole array

like MAP and REDUCE, higher order functions are not really new to Lisp. In Common Lisp they are extended to vectors.

> list languages and array languages are quite different.

There are some common things like interactive use, functional flavor, etc.

There's also April APL: https://github.com/phantomics/april

Also the array language family seems to be stronger than ever with foss: ngn/k, BQN, uiua, and of course J but as you mentioned they're all different languages.

I love J and APL, but April takes the cake for me[1]. APL in Lisp.

I also prefer SPARK2014 instead of Rust if I am not going to use C. I've started learning Rust a few times. SPARK2014 is easier to get going for me, and it has been used to produce high-integrity software and real-world applications for over a decade, and more if you include Ada from which it sprang[2].

[1] https://github.com/phantomics/april

[2] https://www.adacore.com/about-spark

The one big use case was RabbitMQ in a messaging app, not HFT. I doubt Elixir even with Nx can compete with low-level HFT code. Python DL/ML code libraries are just wrappers around C too. Maybe if BeamAsm and Nx are used Elixir could be used for more numerical or not just distributed applications.

I've programmed in Python and Julia, and when I worked at an engineering (mechanical, entertainment engineering) company, Julia was great for its similarity to Matlab. I am a self-taught engineer, so I did not get pulled into Matlab in college.

Personally, I took to Erlang, so I could write plugins for Wings3D back in the early 2000s, but I never stuck with Erlang, or Wings3D (Blender3D was my choice and I even contributed to have it go opensource way back when). I like Erlang's syntax better for some reason, although Elixir's is beautiful too. I was not a Ruby programmer, and I had delved into Haskell and Prolog, so I think Erlang made more sense to me. I think Elixir has a lot more momentum behind it than Erlang, but at the root it's Erlang, so I think I'll stick with Erlang for BEAM apps. My favorite language is April[1] (APL in Lisp), and given my love of J, would be a better fit for any finance apps I might write. I am trying to convert some of the Lisp code in this book, "Professional Automated Trading: Theory and Practice" to April.

Maybe I'll write some equivalent Elixir code to compare.

[1] https://github.com/phantomics/april

I'm also very curious for hear from expert lispers. I've tried to find the sweat spot where lisp would fit better than what I already know: shell for glue and file ops, R for data munging and vis, python to not reinvent things, perl/core-utils for one liners. But before I can find the niche, I get turned off by the amount of ceremony -- or maybe just how different the state and edit/evaluate loop is.

I'm holding onto some things that make common lisp look exciting and useful (static typing[0], APL DSL[1], speed [2,3,4]) and really want to get familiar with structural editing [5]

[0] https://github.com/phantomics/april - APL dsl

The 2 0 at the start of the APL line above controls the mirroring behavior. The second number can be set to 0 or 1 to choose which side of the image to mirror, while the 2 sets the axis along which to mirror. This will be 1 or 2 for a raster image but this function can mirror any rank of array on any axis.

April was used to teach image filtering in a programming class for middle-schoolers, you can see a summary in this video: https://vimeo.com/504928819

For more APL-driven graphics, April's repo includes an ncurses demo featuring a convolution kernel powered by ⌺, the stencil operator: https://github.com/phantomics/april/tree/master/demos/ncurse...

#1: Thanks to J, I was able to get in the global Top 100 in the first day of Advent of Code. I've never done this before and I'm feeling a bit emotional. Thanks, J. #2: April 1.0 Is Released | 4 comments #3: BQNPAD — a BQN REPL with syntax highlighting and live evaluation preview | 8 comments

APL + Lisp =

https://github.com/phantomics/april/ and yes it is used in production©!

> What pushed the development of April really is that April is used by a hardware startup called Bloxl (of which I am the CTO). There are other users but Bloxl is the flagship application.

https://www.arraycast.com/episodes/episode23-andrew-sengul

Bloxl in use: https://user-images.githubusercontent.com/3721004/159686845-... See also the ELS conference 2022.