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Re:JAX – NumPy on the CPU, GPU, and TPU, with great automati... by tehsauce on Friday September 29, 2023 @12:44AM (#37699358)
Jax is super useful for scientific computing. Although nbody sims might not be the best application. A naive nbody sim is very easy to implement and accelerate in jax (here’s my version: https://github.com/PWhiddy/jax-experiments/blob/main/nbody.i...), but it can be tricky to scale it. This is because efficient nbody sims usually either rely on trees or spatial hashing/sorting which are tricky to efficiently implement with jax.
Re:JAX – NumPy on the CPU, GPU, and TPU, with great automati... by mattjjatgoogle on Friday September 29, 2023 @11:53AM (#37706060)
Have you seen JAX MD? https://github.com/jax-md/jax-md
Re:JAX – NumPy on the CPU, GPU, and TPU, with great automati... by tehsauce on Friday September 29, 2023 @02:10PM (#37708052)
I've seen it although haven't dived deep into it. It looks like they have some interesting support for particle cell data structures, but is fairly complicated and carries limitations: https://jax-md.readthedocs.io/en/main/_modules/jax_md/partit...
Re:JAX – NumPy on the CPU, GPU, and TPU, with great automati... by dekhn on Friday September 29, 2023 @02:42PM (#37708494)
Last time I looked at JAX MD it didn't support most of the force field terms necessary for simulating proteins and DNA. For example, it could do n-body simulations with some potential, but not the bonds/torsions between atoms. It's unclear if they added support, but that's a huge gap in functionality compared to other systems.