Vulkan.jl VS Oceananigans.jl

You may be confusing front end APIs and the compiler backends.

Julia is flexible enough that you can essentially define domain specific languages within Julia for certain applications. In this case, we are using Julia as an abstract front end and then deferring the concrete interface to vendor specific GPU compilation drivers. Part of what permits this is that Julia is a LLVM front end and many of the vendor drivers include LLVM-based backends. With some transformation of the Julia abstract syntax tree and the LLVM IR we can connect the two.

That said we are mostly dependent on vendors providing the backend compiler technology. When they do, we can bridge Julia to use that interface. We can wrap Vulkan and technologies like oneAPI.

https://github.com/JuliaGPU/Vulkan.jl

I think it’s also the design philosophy. JuMP and ForwardDiff are great success stories and are packages very light on dependencies. I like those.

The DiffEq library seems to pull you towards the SciML ecosystem and that might not be agreeable to everyone.

For instance a known Julia project that simulates diff equations seems to have implemented their own solver

https://github.com/CliMA/Oceananigans.jl

I‘m currently playing around with Oceananigans.jl (https://github.com/CliMA/Oceananigans.jl). Do you know how both are similar or different?

Oceananigans.jl has really intuitive step-by-step examples and a great discussion page on GitHub.