Newmark local time stepping on high-performance computing architectures
KAUST DepartmentEarth Science and Engineering Program
Extreme Computing Research Center
Physical Science and Engineering (PSE) Division
Online Publication Date2016-11-25
Print Publication Date2017-04
Permanent link to this recordhttp://hdl.handle.net/10754/621888
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AbstractIn multi-scale complex media, finite element meshes often require areas of local refinement, creating small elements that can dramatically reduce the global time-step for wave-propagation problems due to the CFL condition. Local time stepping (LTS) algorithms allow an explicit time-stepping scheme to adapt the time-step to the element size, allowing near-optimal time-steps everywhere in the mesh. We develop an efficient multilevel LTS-Newmark scheme and implement it in a widely used continuous finite element seismic wave-propagation package. In particular, we extend the standard LTS formulation with adaptations to continuous finite element methods that can be implemented very efficiently with very strong element-size contrasts (more than 100×). Capable of running on large CPU and GPU clusters, we present both synthetic validation examples and large scale, realistic application examples to demonstrate the performance and applicability of the method and implementation on thousands of CPU cores and hundreds of GPUs.
CitationRietmann M, Grote M, Peter D, Schenk O (2016) Newmark local time stepping on high-performance computing architectures. Journal of Computational Physics. Available: http://dx.doi.org/10.1016/j.jcp.2016.11.012.
SponsorsThe computational resources and services used in this work were provided by the Swiss National Supercomputing Centre (CSCS). D. Peter and M. Rietmann were supported by the Swiss PASC project “A framework for multi-scale seismic modelling and inversion.” SPECFEM3D_Cartesian is hosted by the Computational Infrastructure for Geodynamics (CIG) which is supported by the National Science Foundation award NSF-0949446.
JournalJournal of Computational Physics