skiko VS criterium

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.

(criterium does not work here yet b.t.w., but it probably will be working soon)

criterium, specifically the quick-bench function, will actually run multiple samples an provide a mean runtime (as well as other useful stats) so you can get an idea of what a jit'd warmed up performance looks like. time is great in a pinch, but you end up needing to run it multiple times to ensure optimizations are kicking and and other artifacts (like gc) aren't throwing the results.

I'm going to leave tooling out of this and run everything through a repl on the command line right from the jar. One other thing I want to do is include the incredible criterium library so we can profile. I'm deliberately including criterium separately like this because you shouldn't have a dev-time tool like criterium in an uberjar. And knowing how to easily combine other jars with your real production jar can be very helpful. I grabbed the jar from my .m2 cache.

I am just going to leave this here - https://github.com/hugoduncan/criterium

where crit is criterium. As you can see, you're spending most of your time in the seq transformation part.

It's better to benchmark with something like criterium. time is a bit inaccurate. Though, if it's really 15 seconds, I guess will not be that big of a difference

>>> One of Clojure's biggest weaknesses in practice is that breaking in to those functional structures to figure out where the time is being spent or to debug them is harder than in other languages. This is a natural trade-off of developing a terse and powerful language.

Not that hard if you use something like YourKit. There's also a quite good Clojure library https://github.com/hugoduncan/criterium .

Criterium (the benchmarking library used here) uses multiple runs to obtain tighter bounds on amortized performance, as well as techniques to amortize the effects of garbage collection and JIT compilation. See https://github.com/hugoduncan/criterium for a brief overview, as well as links to the pitfalls and statistical techniques involved in JVM benchmarking.