期刊
COMPUTERS & FLUIDS
卷 173, 期 -, 页码 264-272出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compfluid.2018.01.040
关键词
Space filling curve; SFC; Mesh partitioning; Geometric partitioning; Parallel computing
资金
- Ministerio de Economia, Industria y Competitividad, of Spain [TRA2017-88508-R]
- European Union Horizon 2020 Programme
- Brazilian Ministry of Science, Technology and Innovation through Rede Nacional de Pesquisa (RNP), under the HPC4E Project [689772]
- National Science Foundation
- National Science Foundation [OCI 07-25070, ACI-1238993]
- state of Illinois
- PRACE preparatory access projects - EU Horizon 2020 research and innovation programme [653838]
- Consejo Nacional de Ciencia y Tecnologa (CONACyT, Mexico) [231588 290790]
- Juan de la Cierva [IJCI-2014-21034, IJCI-2015-26686]
Larger supercomputers allow the simulation of more complex phenomena with increased accuracy. Eventually this requires finer and thus also larger geometric discretizations. In this context, and extrapolating to the Exascale paradigm, meshing operations such as generation, deformation, adaptation regeneration or partition/load balance, become a critical issue within the simulation workflow. In this paper we focus on mesh partitioning. In particular, we present a fast and scalable geometric partitioner based on Space Filling Curves (SFC), as an alternative to the standard graph partitioning approach. We have avoided any computing or memory bottleneck in the algorithm, while we have imposed that the solution achieved is independent (discounting rounding off errors) of the number of parallel processes used to compute it. The performance of the SFC-based partitioner presented has been demonstrated using up to 4096 CPU-cores in the Blue Waters supercomputer. (C) 2018 Elsevier Ltd. All rights reserved.
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