JWM VS magicl

sigh

Yeah. I am very bullish on Kotlin. Think it's probably the most exciting language evolving right now.

I went on a few-tweet minirant here about why:

https://twitter.com/GavinRayDev/status/1443279425311805440

But the tl;dr is that:

- There is Jetpack Compose currently, for Desktop, Web, and Android

- And Kotlin Native putting a large portion of resources into Skia bindings (JetBrains calls the lib "Skiko" for Kotlin Native https://github.com/JetBrains/skiko and "Skija")

It's very clear (and there are some employees which have confirmed this IIRC) that they are working on "Jetpack Compose Everywhere" that runs on iOS as well, from a single codebase.

There's the big Kotlin event going on right now, where they just announced the new WASM backend and changes in their compiler + IR commonizing/restructuring ("K2").

- https://blog.jetbrains.com/kotlin/2021/10/the-road-to-the-k2...

- https://www.youtube.com/watch?v=-pqz9sKXatw

The net result is that you wind up with a single language that you can use to write your backend API, your UI code (Jetpack Compose app deployed across Web/Android/iOS/Mac/Win/Linux, or transpile to JS/TS if you just want a web app, etc) and with Kotlin Native even your native, low-level code to integrate with existing C/C++ etc ecosystem.

KN already does automatic bindgen for C and Swift headers, they have direct C++ interop (like Swift does) on their future roadmap as a potential "todo".

All of this is mostly possible already -- I can do the same thing using IE Java, GraalVM, and a transpiler like Google's j2cl or bck2brwser (which is what Gluon uses for JavaFX on the web). Including the "native" part.

IE, here's a contribution I made to get GraalVM producing native binaries using Skia from the JVM + JNI Jetbrains Skia library:

https://github.com/HumbleUI/JWM/issues/158

But Kotlin is pushing the hardest to make this whole platform/stack from native <-> desktop <-> mobile <-> browser a seamless, unified experience. And you can feel it, when you try to do the "whole stack, every platform, one language" thing.

Sorry for the rant and wall of text!

(Link didn't work for me)

https://github.com/quil-lang/magicl/blob/master/src/high-lev...

use MAGICL. [1] It is optionally and transparently accelerated by BLAS/LAPACK.

[1] https://github.com/quil-lang/magicl/blob/master/doc/high-lev...

Maybe have a look at how magicl does this?

This is essentially the first option, which is already supported by MAGICL by loading MAGICL/EXT-LAPACK [1].

[1] https://github.com/quil-lang/magicl#extensions

I think what the magicl team has done is brilliant - allowing multiple implementations is awesome.

Second up is magicl, especially useful if performance is a concern. This might not be as extensive as numcl, but it's been battle tested in the industry over the last decade or so. Because this uses generic functions, so long as you are using not-very-small arrays, performance should not be a concern for you. And even if you are, you could write your own functions that use the low-level functions that magicl's backends define. Otherwise performance can be at par with numpy.

This Dev How-To describes (I hope in enough detail) how to add these specialized routines to MAGICL.

I agree... and I do don't want be the person who has not rallied. I just took a look at guicho's issue from 2019. And here, you yourself have admitted that the high level interface is less than ideal and needs more work. However, the very point that magicl is an industry standard could imply that potentially radical backward-incompatible changes can be hard. But, honestly, I want to discuss this, time permitting!

Common Lisp is a great language to build new tools for data science, but currently has pretty awful library support existing data science workflows. Common Lisp is sorely lacking in high-quality statistics, plotting, and sparse arrays. There’s been a long work-in-progress library to bring flexible and high-performance linear algebra to Lisp, but it needs more contributors.