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Journal of Scientific Computing

, Volume 61, Issue 2, pp 258–280 | Cite as

Parallel Domain Decomposition Methods with Mixed Order Discretization for Fully Implicit Solution of Tracer Transport Problems on the Cubed-Sphere

  • Haijian Yang
  • Chao Yang
  • Xiao-Chuan CaiEmail author
Article

Abstract

In this paper, a fully implicit finite volume Eulerian scheme and a corresponding scalable parallel solver are developed for some tracer transport problems on the cubed-sphere. To efficiently solve the large sparse linear system at each time step on parallel computers, we introduce a Schwarz preconditioned Krylov subspace method using two discretizations. More precisely speaking, the higher order method is used for the residual calculation and the lower order method is used for the construction of the preconditioner. The matrices from the two discretizations have similar sparsity pattern and eigenvalue distributions, but the matrix from the lower order method is a lot sparser, as a result, excellent scalability results (in total computing time and the number of iterations) are obtained. Even though Schwarz preconditioner is originally designed for elliptic problems, our experiments indicate clearly that the method scales well for this class of purely hyperbolic problems. In addition, we show numerically that the proposed method is highly scalable in terms of both strong and weak scalabilities on a supercomputer with thousands of processors.

Keywords

Transport equation Cubed-sphere Fully implicit method  Domain decomposition Parallel scalability 

Notes

Acknowledgments

The authors would like to express their appreciations to the anonymous reviewers for the invaluable comments that greatly improved the quality of the manuscript. This work was supported in part by NSF grant CCF-1216314 and DOE grant DE-SC0001774. H. Yang was also supported in part by NSFC grants 91330111, 11201137 and 11272352. C. Yang was also supported in part by NSFC grants 61170075 and 91130023

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.College of Mathematics and EconometricsHunan UniversityHunanPeople’s Republic of China
  2. 2.Laboratory of Parallel Software and Computational Science, Institute of SoftwareChinese Academy of SciencesBeijingPeople’s Republic of China
  3. 3.State Key Laboratory of Computer Science, Institute of SoftwareChinese Academy of SciencesBeijingPeople’s Republic of China
  4. 4.Department of Computer ScienceUniversity of Colorado BoulderBoulderUSA

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