Abstract
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.
| Original language | English |
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| Title of host publication | Proceedings of SC 2019 |
| Subtitle of host publication | The International Conference for High Performance Computing, Networking, Storage and Analysis |
| Publisher | IEEE Computer Society |
| ISBN (Electronic) | 9781450362290 |
| DOIs | |
| State | Published - Nov 17 2019 |
| Event | 2019 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2019 - Denver, United States Duration: Nov 17 2019 → Nov 22 2019 |
Publication series
| Name | International Conference for High Performance Computing, Networking, Storage and Analysis, SC |
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| ISSN (Print) | 2167-4329 |
| ISSN (Electronic) | 2167-4337 |
Conference
| Conference | 2019 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2019 |
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| Country/Territory | United States |
| City | Denver |
| Period | 11/17/19 → 11/22/19 |
Funding
This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan(http://energy.gov/downloads/doe-public-access-plan). This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725. This work was performed under the auspices of the U.S. Department of Energy by LLNL under Contract DE-AC52-07NA27344. Support was provided by the LLNL Laboratory Directed Research and Development (LDRD) program. Research reported in this publication was supported by the Office of the Director, National Institutes Of Health under Award Number DP5OD019876. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Support was provided by the Hartwell Foundation and Duke Theo Pilkington Fellowship. We thank all the members of the Randlelab for their careful review and feedback on this work.
Keywords
- Bandwidth
- Lattice boltzmann method
- Memory
- Moment representation