cloc VS Co-dfns

Cloc - https://github.com/AlDanial/cloc

and get the results on the cli.

Let me know what you think. :)

0: https://github.com/AlDanial/cloc

Awesome stuff, thank you! I‘d love some flags/options for cloc integration if it can be detected, maybe a summary of the top N languages for directories (67% Rust, 13% Html, 9% Bash) or something. Just a suggestion/idea. Gonna install it anyway, it‘s shiny!

The solution I am using is currently comprised of 145 projects, 141k+ lines of C#, and 37k+ lines of Razor, courtesy of cloc:

Just out of curiosity, I downloaded the latest version of GNU coreutils and compared the line count between a few source files and the resulting disassembled object files (using cloc to exclude blank lines and comments). It looks like the ratio is very approximately 2 assembly instructions per line of C code. Obviously, that will depend a lot on what the code is doing and the coding style.

When I started making a game [0] last year, first thing I did was write a little Unity script that takes a screenshot of the opening scene, counts current lines of code using CLOC [1] (for fun, not as a true measure of anything), and occasionally renders it all out to an image file.

With that I'm able to create some pretty fun time lapses of progress. I've been doing this at an arbitrary milestone, whenever my Luau [2] LOC surpasses C++ by another factor. This post reminded me I'm overdue for another now that Luau > 3x C++ LOC.

I find it rewarding to look back at my progress. I'll share in case it's interesting for you too [3].

[0] https://store.steampowered.com/app/2168330/Helmscape/

[1] https://github.com/AlDanial/cloc

[2] https://luau-lang.org

[3] https://twitter.com/kineticpoet/status/1619508466212831232

My manager just asked me about this a few days ago (sigh) cloc is good for this - you can pass it a hash or two hashes and it will give you counts accordingly. https://github.com/AlDanial/cloc

Finally, since I'm limited on the character-length of this post, I'll post an individual comment for each year with a table of data. The "All Rank" column will rank the problem by difficulty (measured by leaderboard close time) across all years, with 1 being longest. The "Yr Rank" column will be similar, but ranked only within that year. The "P1 LOC" and "P2 LOC" columns show the numbers of lines of code in my solutions for each part as measured by cloc (each part is stand-alone so there will be some duplication, especially for Intcode). Other columns should be self-explanatory.

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.