Oceananigans.jl
pyhpc-benchmarks
Our great sponsors
| Oceananigans.jl | pyhpc-benchmarks | |
|---|---|---|
| 4 | 6 | |
| 875 | 301 | |
| 1.6% | - | |
| 9.5 | 3.2 | |
| 5 days ago | 4 months ago | |
| Julia | Python | |
| MIT License | The Unlicense |
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.
Oceananigans.jl
-
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
-
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
pyhpc-benchmarks
-
Supercharged high-resolution ocean simulation with Jax
True, but unfortunately Pytorch is not quite there yet when it comes to more complex benchmarks:
https://github.com/dionhaefner/pyhpc-benchmarks#example-resu...
JAX really is the only library that comes close to low-level code on CPU, almost always.
-
[D] Does working with Tensorflow affect my chances of getting research internships?
https://github.com/dionhaefner/pyhpc-benchmarks begs to differ.
- GitHub - dionhaefner/pyhpc-benchmarks: A suite of benchmarks for CPU and GPU performance of the most popular high-performance libraries for Python
- HPC Benchmarks for Python
- Pyhpc: Benchmarks for CPU and GPU of the most popular high-perf Python libs
What are some alternatives?
MATDaemon.jl
tf-quant-finance - High-performance TensorFlow library for quantitative finance.
FiniteDiff.jl - Fast non-allocating calculations of gradients, Jacobians, and Hessians with sparsity support
pyopencl - OpenCL integration for Python, plus shiny features
MITgcm - M.I.T General Circulation Model master code and documentation repository
sqloxide - Python bindings for sqlparser-rs
Metal.jl - Metal programming in Julia
opendylan - Open Dylan compiler and IDE
3d-ken-burns - an implementation of 3D Ken Burns Effect from a Single Image using PyTorch
julia-ml-from-scratch - Machine learning from scratch in Julia
XLA.jl - "Maybe we have our own magic."