I did @officialBinotto's @ScuderiaFerrari SF71H in @FluidX3D #CFD on a supercomputer.
- 1s in real life @ 100km/h
- 20s 4K60 video (3x)
- 14h compute on 8x @AMDInstinct #MI200 64GB #GPU
- 144TB data visualized
What I found is absolutely wild. A #SimulationFriday #F1 thread: 🧵1/5
The @FluidX3D simulation was done at 10 billion voxel grid resolution (2152×4304×1076), over 217k time steps (1 second), at Re=3.75M (100km/h).
The fins on the front spoiler create a turbulent boundary layer and kick up it up onto the front wheels to reduce drag. 🧵2/5
The streamlined chassis guides airflow under the spoiler to create down force. The halo - one of the best additions to the sport in terms of safety - is rather aerodynamic.
Each frame of the video is 120GB, 144TB for 1201 frames. @FluidX3D renders the data directly in VRAM. 🧵3/5
With commercial #CFD software, where a licence can cost $120k, such a detailed simulation would take months. I did it in 14 hours, including rendering, on 8 #GPU​s. I have put the C++/#OpenCL source code of @FluidX3D on @github, for free: github.com/ProjectPhysX/F…
The 3D model is from Rafael Rodrigues: thingiverse.com/thing:2990512/…
The video is available on YouTube in 4K60, along with more details on the simulation setup and methods:

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More from @ProjectPhysX

Jul 27
The #MI250 is misleadingly marketed as "one chiplet GPU" with 13312C, 90TFLOPs & 128GB @ 3.2TB/s.

But it is not. The 2 GCDs are 2 separate GPUs with 64GB each, like a K80 dual-GPU but in a socket. One #GPU can't directly access the other's memory.

To use both GCDs, the software needs to be multi-GPU capable. For many algorithms this is very difficult and for some it is entirely infeasible. The desire for large unified memory is huge.
The #MI250 promises exactly that with "128GB", but delivers only half.
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