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

Pedro Valero-Lara; Jeffrey Vetter; John Gounley; Amanda Randles

doi:10.1145/3624062.3624250

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

Pedro Valero-Lara, Jeffrey Vetter, John Gounley, Amanda Randles

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

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.

Original language	English
Title of host publication	Proceedings of 2023 SC Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023
Publisher	Association for Computing Machinery
Pages	1697-1704
Number of pages	8
ISBN (Electronic)	9798400707858
DOIs	https://doi.org/10.1145/3624062.3624250
State	Published - Nov 12 2023
Event	2023 International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023 - Denver, United States Duration: Nov 12 2023 → Nov 17 2023

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	2023 International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023
Country/Territory	United States
City	Denver
Period	11/12/23 → 11/17/23

Funding

This research used resources of the Oak Ridge Leadership Computing Facility and the Experimental Computing Laboratory at the Oak Ridge National Laboratory, which is supported by DOE’s Office of Science under Contract No. DE-AC05-00OR22725. This research was supported in part by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the DOE’s Office of Science and the National Nuclear Security Administration. This manuscript has been authored by UT-Battelle LLC under Contract No. DE-AC05-00OR22725 with the DOE. The publisher, by accepting the article for publication, acknowledges that the US Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of the manuscript or allow others to do so, for US Government purposes. The DOE will provide public access to these results in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Funders	Funder number
U.S. Department of Energy
Office of Science	17-SC-20-SC, DE-AC05-00OR22725
National Nuclear Security Administration
UT-Battelle

Keywords

AMD
CUDA
Computational Fluid Dynamics
GPU
HIP
Lattice Boltzmann Method
NVIDIA

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10.1145/3624062.3624250

Cite this

Valero-Lara, P., Vetter, J., Gounley, J., & Randles, A. (2023). Moment Representation of Regularized Lattice Boltzmann Methods on NVIDIA and AMD GPUs. In Proceedings of 2023 SC Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023 (pp. 1697-1704). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3624062.3624250

Valero-Lara, Pedro ; Vetter, Jeffrey ; Gounley, John et al. / Moment Representation of Regularized Lattice Boltzmann Methods on NVIDIA and AMD GPUs. Proceedings of 2023 SC Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023. Association for Computing Machinery, 2023. pp. 1697-1704 (ACM International Conference Proceeding Series).

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abstract = "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.",

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Valero-Lara, P , Vetter, J , Gounley, J & Randles, A 2023, Moment Representation of Regularized Lattice Boltzmann Methods on NVIDIA and AMD GPUs. in Proceedings of 2023 SC Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 1697-1704, 2023 International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023, Denver, United States, 11/12/23. https://doi.org/10.1145/3624062.3624250

Moment Representation of Regularized Lattice Boltzmann Methods on NVIDIA and AMD GPUs. / Valero-Lara, Pedro ; Vetter, Jeffrey ; Gounley, John et al.
Proceedings of 2023 SC Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023. Association for Computing Machinery, 2023. p. 1697-1704 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Valero-Lara P , Vetter J , Gounley J, Randles A. Moment Representation of Regularized Lattice Boltzmann Methods on NVIDIA and AMD GPUs. In Proceedings of 2023 SC Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023. Association for Computing Machinery. 2023. p. 1697-1704. (ACM International Conference Proceeding Series). doi: 10.1145/3624062.3624250

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

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