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Moment Representation of Regularized Lattice Boltzmann Methods on NVIDIA and AMD GPUs

by Pedro Valero Lara, Jeffrey S Vetter, John P Gounley, Amanda Randles
Publication Type
Conference Paper
Book Title
SC-W '23: Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis
Publication Date
Page Numbers
1697 to 1704
Publisher Location
New York, New York, United States of America
Conference Name
ScalAH23: 14th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Heterogeneous Systems
Conference Location
Denver, Colorado, United States of America
Conference Sponsor
Conference Date

The lattice Boltzmann method is a highly scalable Navier-Stokes solver that has been applied to flow problems in a wide array of domains. However, the method is bandwidth-bound on modern GPU accelerators and has a large memory footprint. In this paper, we present new 2D and 3D GPU implementations of two different regularized lattice Boltzmann methods, which are not only able to achieve an acceleration of ∼ 1.4 × w.r.t. reference lattice Boltzmann implementations but also reduce the memory requirements by up to 35% and 47% in 2D and 3D simulations respectively. These new approaches are evaluated on NVIDIA and AMD GPU architectures.