Article in Proceedings INPROC-2016-43

BibliographyBakhtiari, Arash; Malhotra, Dhairya; Raoofy, Amir; Mehl, Miriam; Bungartz, Hans-Joachim; Biros, George: A Parallel Arbitrary-order Accurate AMR Algorithm for the Scalar Advection-diffusion Equation.
In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis.
University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology.
pp. 1-12, english.
Piscataway, NJ, USA: IEEE Press, November 2016.
ISBN: 978-1-4673-8815-3.
Article in Proceedings (Conference Paper).
CorporationSC '16, Salt Lake City, Utah
CR-SchemaJ.0 (Computer Applications General)
Keywordsadaptive mesh refinement, semi-Lagrangian, fast multipole, parallel computing
Abstract

We present a numerical method for solving the scalar advection-diffusion equation using adaptive mesh re- finement. Our solver has three unique characteristics: (1) it supports arbitrary-order accuracy in space; (2) it allows different discretizations for the velocity and scalar advected quantity; and (3) it combines the method of characteristics with an integral equation formulation. In particular, our solver is based on a second-order accurate, unconditionally stable, semi-Lagrangian scheme combined with a spatially-adaptive Chebyshev octree for discretization. We study the convergence, single-node perfor- mance, strong scaling, and weak scaling of our scheme for several challenging flows that cannot be resolved efficiently without using high-order accurate discretizations. For example, we consider problems for which switching from 4th order to 14th order approximation results in two orders of magnitude speedups for a fixed accuracy. For our largest run, we solve a problem with one billion unknowns on a tree with maximum depth equal to 10 and 14th-order elements on 16,384 cores on the ”STAMPEDE” system at the Texas Advanced Computing Center.

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Department(s)University of Stuttgart, Institute of Parallel and Distributed Systems, Simulation of Large Systems
Entry dateNovember 30, 2016
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