# A new GPU implementation for lattice-Boltzmann simulations on sparse geometries

arXiv: https://arxiv.org/abs/1611.02445 · HTML: https://mlsanity.com/paper/1611.02445/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2016.

## Built on by (most cited first, 1 of 1)

- [Physics-informed convolutional neural networks for fluid flow through porous media](https://mlsanity.com/paper/2605.20250/) (2026)

## Abstract (arXiv)

We describe a high-performance implementation of the lattice Boltzmann method (LBM) for sparse 3D geometries on graphic processors (GPU). The main contribution of this work is a data layout that allows to minimise the number of redundant memory transactions during the propagation step of LBM. We show that by using a uniform mesh of small three-dimensional tiles and a careful data placement it is possible to utilise more than 70% of maximum theoretical GPU memory bandwidth for D3Q19 lattice and double precision numbers. The performance of our implementation is thoroughly examined and compared with other GPU implementations of LBM. The proposed method performs the best for sparse geometries with good spatial locality.