bakeware VS taichi

I've not used either of them myself, but I think they fit some of your requirements (simple programs, efficient, events, no memory management). There seem to be libraries for constraint programming. It does run on a VM with a GC though. And while programs can be compiled to binaries, they're not tiny.

There’s a couple of approaches to this problem going on:

1. Bakeware “bakes” your application together with the entire Erlang/OTP/Beam/Elixir stack into a single binary. Given the “batteries included” philosophy of OTP, these binaries end up being fairly large, but it works: https://github.com/bake-bake-bake/bakeware

2. Lumen compiles Elixir, Erlang and friends into WASM. This will in time enable small fast cross-platform static binaries, but it is not done yet: https://getlumen.org/

That one is out of date, the one it forked of is not: https://github.com/bake-bake-bake/bakeware

I've read that the Javascript runtime Deno is able to compile to a static binary and has a standard lib that is practically a port of Gos standard library....i feel the static binary deal is quite a game changer today given its portability in devops ....Elixir is able to compile to static binaries as well with bakeware 😊https://github.com/bake-bake-bake/bakeware

It's already possible with https://github.com/bake-bake-bake/bakeware ✌️😊

taichi – Productive, portable, and performant GPU programming in Python

ETH Zurich is using it for their physics sim courses, University of Utah is using it for simulations (SIGGRAPH 2022), OPPO (they make smart devices running Android), Kuaishou uses it for liquid and gas simulation on GPUs. Lots of GPU accelerated sim stuff.

https://www.taichi-lang.org/

https://www.researchgate.net/publication/337118128_Taichi_a_...

https://github.com/taichi-dev/taichi

Check our the realease note (https://github.com/taichi-dev/taichi/releases) for more improvements.

I've recently started using Taichi (https://taichi-lang.org/) for numerical codes and the fact it doesn't try to trick you into thinking it's numpy is a nice "feature". ;)

Taichi v1.4.0 is released! See what's new: - Taichi AOT, along with a native Taichi Runtime library: Native applications can now load compiled AOT modules and launch Taichi kernels without a Python interpreter. - Taichi ndarray: An array object that holds contiguous multi-dimensional data to allow easy data exchange with external libraries. - Dynamic index: Use variable indices whenever necessary on all backends without affecting the performance of those matrices with only constant indices. See deprecation and more improvements in the release note.

Oh, then if you're not already tied to any particular VFX software, I might as well recommend Taichi again.

It's an upgrade of a basic version. See changes to the source code here: https://github.com/taichi-dev/taichi/pull/6851