JWM VS cl-cuda

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!

> You can write a lot of macrology to get around it, but there's a point where you want actual compiler writers to be doing this

this is not the job of compiler writers (although writing macros is akin to writing a compiler but i do not think that this is what you mean). in julia the numerical programming packages are not part of the standard library and a lot of it is wrappers around C++ code especially when the drivers to the underlining hardware are closed-source [0]. also here is the similar library in common lisp [1]

[0] https://github.com/JuliaGPU/CUDA.jl

[1] https://github.com/takagi/cl-cuda

A Common Lisp Library to Use Nvidia CUDA\ (0 comments)

Personally, I've been relying on the stream-based method using py4cl/2, mostly because I did not - and perhaps do not - have the knowledge and time to dig into the CFFI based method. The limitation is that this would get you less than 10000 python interactions per second. That is sufficient if you will be running a long running python task - and I have successfully run trivial ML programs using it, but any intensive array processing gets in the way. For this later task, there are a few emerging libraries like numcl and array-operations without SIMD (yet), and numericals using SIMD. For reasons mentioned on the readme, I recently cooked up dense-arrays. This has interchangeable backends and can also use cl-cuda. But barring that, the developer overhead of actually setting up native-CFFI ecosystem is still too high, and I'm back to py4cl/2 for tasks beyond array processing.