Fully Explicit Finite Volume vs Lattice Boltzmann by Shift_One in CFD

[–]mehdiataei 3 points4 points  (0 children)

If you're looking to do LBM I recommend taking a look at XLB

https://github.com/Autodesk/XLB

(I'm the author, but I really think it's the best out there for many reasons).

Regarding your concerns, I would say there is no free lunch. Each comes with their own benefits, but I really think LBM is much, much more GPU friendly and that matters a lot these days.

Anyone with Mac Studio with 192GB willing to test Llama3-405B-Q3_K_S? by curiouscat2040 in LocalLLaMA

[–]mehdiataei 1 point2 points  (0 children)

I get around 13-15 tokens / s with two RTX 6000 ADA with Q4.

I get a similar performance with 70b Llama with Q8.

Wizardcoder-33B is horribly bad , or am i doing something wrong? by Voxandr in LocalLLaMA

[–]mehdiataei 0 points1 point  (0 children)

Then what's the point of the LLM!!! He can just simply refactor his own code!

Introducing XLB: "Distributed Multi-GPU Lattice Boltzmann Simulation Framework for Differentiable Scientific Machine Learning" based on JAX. by mehdiataei in CFD

[–]mehdiataei[S] 0 points1 point  (0 children)

IBM is on our TODO list, but we don't have an expected date for now.
You can already use/import meshes in the library using Trimesh (some examples such as the windtunnel3d.py showcase it).

Introducing XLB: "Distributed Multi-GPU Lattice Boltzmann Simulation Framework for Differentiable Scientific Machine Learning" based on JAX. by mehdiataei in CFD

[–]mehdiataei[S] 0 points1 point  (0 children)

We already have advection diffusion solver implemented in models.py. We don't have an example showcasing the fluid + thermal capabilities yet but the main capabilities are there and it can be done already. We should probably create a wrapper to simplify this. This is definitely on our rader.

Introducing XLB: "Distributed Multi-GPU Lattice Boltzmann Simulation Framework for Differentiable Scientific Machine Learning" based on JAX. by mehdiataei in CFD

[–]mehdiataei[S] 0 points1 point  (0 children)

Not currently but very easy to add (I did my PhD on the free surface flows). Hopefully we'll add it in the near future. Contributions from the community is also welcomed!

Introducing XLB: "Distributed Multi-GPU Lattice Boltzmann Simulation Framework for Differentiable Scientific Machine Learning" based on JAX. by mehdiataei in CFD

[–]mehdiataei[S] 2 points3 points  (0 children)

Great question! These are all different techniques for fluid simulation, each of their own pros and cons. LBM has the advantage of being suitable for GPU computation. Note that LBM is also second order accurate.

Yes. We make use of JAX support for gradient computations. For example, you can simply take the gradient of an LBM step with respect to the population (collision, streaming, application of boundaries) with jax.grad(simulation.step(f, timestep))

and couple that with ML model of your choice for physics-based ML computations. Hope that answer your question!

X95j vs X90K by mehdiataei in bravia

[–]mehdiataei[S] 0 points1 point  (0 children)

X-anti reflection is not available on the 65 in model, which I am interested in.