skiko VS cl-cuda

All I know is that they use skiko.

It built on a render framework named skia, JetBrains create a kotlin mapping which named skiko for it.

I was curious to give it a go but unusable on Gnome 42.5; the window lags so much that simply trying to drag to reposition it on the screen is impossible seems related an issue with Skiko and OpenGL reported here: https://github.com/JetBrains/skiko/issues/494

Fleet does not use Compose, but it does use Skiko[1], which also provides binding for Skia[2] (the native graphics library also used by Chrome & Flutter).

The main difference between the libraries is that Skija provides Java/JVM bindings for Skia, whereas Skiko provides Kotlin bindings for Kotlin/JVM, Kotlin/JS, and Kotlin/Native targets. Of course Skiko's Kotlin/JVM bindings can be used with other JVM languages, not just with Kotlin.

[1] https://github.com/JetBrains/skiko

[2] https://skia.org/

How about skiko?

JetBrains is moving from skija to skiko, but it could be interesting either way!

They Merged today the functions that will be usaged on Compose for opening, closing and handling text inputs https://github.com/JetBrains/skiko/pull/455 (if I understand correctly). Currently, the examples rely on xcodegen file, based on the currently plugin that Compose has for Desktop, it provide alot of things including packaging for desktop, I think they will provide a xcodegen file automatically and use xcodegen behind the scenes to generate everything needed.

https://github.com/JetBrains/skiko (a Kotlin MPP binding for Skia). Skiko Does not contain build for iOS. Now Skiko contains build for iOS, MAC arm processor via Kotlin/Native. So we can use Skiko for pure canvas rendering in all platform. It is matter of time they release compose for iOS. So UI also goes multiplatform.

But it does seem like the solution they're using for Fleet is different. Skia is the same graphics library used by Chromium and Flutter. I'll remain cautiously optimistic for now, but the quality of the text rendering and customization options will make or break it for me personally.

> 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.