Moment Representation of Regularized Lattice Boltzmann Methods on NVIDIA and AMD GPUs
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.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-00OR22725
- OSTI ID:
- 2224172
- Resource Relation:
- Conference: ScalAH23: 14th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Heterogeneous Systems - Denver, Colorado, United States of America - 11/13/2023 10:00:00 AM-11/13/2023 10:00:00 AM
- Country of Publication:
- United States
- Language:
- English
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