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Parallel mesh partitioning based on space filling curves

Cita com:
hdl:2117/125789
Document typeArticle
Defense date2018-09
PublisherElsevier
Rights accessOpen Access
This work is protected by the corresponding intellectual and industrial property rights.
Except where otherwise noted, its contents are licensed under a Creative Commons license
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Attribution-NonCommercial-NoDerivs 3.0 Spain
ProjectMETODOS DE ALTA PRECISION PARA EL DISEÑO DE AERONAVES DE NUEVA GENERACION (AEI-TRA2017-88508-R)
HPC4E - HPC for Energy (EC-H2020-689772)
PRACE-4IP - PRACE 4th Implementation Phase Project (EC-H2020-653838)
HPC4E - HPC for Energy (EC-H2020-689772)
PRACE-4IP - PRACE 4th Implementation Phase Project (EC-H2020-653838)
Abstract
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.
CitationBorrell, R. [et al.]. Parallel mesh partitioning based on space filling curves. "Computers & Fluids", Setembre 2018, vol. 173, p. 264-272.
ISSN0045-7930
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