Moment representation in the lattice Boltzmann method on massively parallel hardware

Published

Conference Paper

© 2019 ACM. The widely-used lattice Boltzmann method (LBM) for computational fluid dynamics is highly scalable, but also significantly memory bandwidth-bound on current architectures. This paper presents a new regularized LBM implementation that reduces the memory footprint by only storing macroscopic, moment-based data. We show that the amount of data that must be stored in memory during a simulation is reduced by up to 47%. We also present a technique for cache-aware data re-utilization and show that optimizing cache utilization to limit data motion results in a similar improvement in time to solution. These new algorithms are implemented in the hemodynamics solver HARVEY and demonstrated using both idealized and realistic biological geometries. We develop a performance model for the moment representation algorithm and evaluate the performance on Summit.

Full Text

Duke Authors

Cited Authors

  • Vardhan, M; Gounley, J; Hegele, L; Draeger, EW; Randles, A

Published Date

  • November 17, 2019

Published In

Electronic International Standard Serial Number (EISSN)

  • 2167-4337

International Standard Serial Number (ISSN)

  • 2167-4329

International Standard Book Number 13 (ISBN-13)

  • 9781450362290

Digital Object Identifier (DOI)

  • 10.1145/3295500.3356204

Citation Source

  • Scopus