Visualizing 144 TeraByte of CFD data for fun

FluidX3D source code: The 20 seconds of video show the car driving at 100km/h for 1 second. Mattia Binotto’s Ferrari SF71H in #CFD. In this 10 billion voxel #FluidX3D simulation you see the wild aerodynamic optimization for a very successful F1 car. #OpenCL compute (2152×4304×1076 resolution grid, 217k time steps) plus rendering 3x 20s 4K60 video took 14 hours. Shown is velocity-colored Q-criterion isosurfaces with marching-cubes. Reynolds number is Million with Smagorinsky-Lilly subgrid model. How is it possible to squeeze 10 billion grid points in only 512GB VRAM? I’m using two techniques here, which together form the holy grail of lattice Boltzmann, cutting memory demand down to only 55 Bytes/node for D3Q19 LBM, or 1/3 of conventional codes: 1. In-place streaming with Esoteric-Pull. This almost cuts memory demand in half and slightly increases performance due to implicit bounce-back boundaries. Paper: https://