Metal.jl
Metal programming in Julia (by JuliaGPU)
Oceananigans.jl
🌊 Julia software for fast, friendly, flexible, ocean-flavored fluid dynamics on CPUs and GPUs (by CliMA)
| Metal.jl | Oceananigans.jl | |
|---|---|---|
| 1 | 4 | |
| 330 | 883 | |
| 2.1% | 1.6% | |
| 8.9 | 9.5 | |
| 10 days ago | 1 day ago | |
| Julia | Julia | |
| MIT License | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Metal.jl
Posts with mentions or reviews of Metal.jl.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-05-27.
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What Apple hardware do I need for CUDA-based deep learning tasks?
If you are really committed to running on Apple hardware then take a look at Tensorflow for macOS. Another option is the Julia programming language which has very basic Metal support at a CUDA-like level. FluxML would be the ML framework in Julia. I’m not sure either option will be painless or let you do everything you could do with a Nvidia GPU.
Oceananigans.jl
Posts with mentions or reviews of Oceananigans.jl.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-12-27.
-
Julia 1.10 Released
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
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GPU vendor-agnostic fluid dynamics solver in Julia
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.
- Supercharged high-resolution ocean simulation with Jax
What are some alternatives?
When comparing Metal.jl and Oceananigans.jl you can also consider the following projects:
Flux.jl - Relax! Flux is the ML library that doesn't make you tensor
MATDaemon.jl