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Computer Architecture and High Performance Computing, Symposium on (2012)
New York, NY, USA USA
Oct. 24, 2012 to Oct. 26, 2012
ISSN: 1550-6533
ISBN: 978-1-4673-4790-7
pp: 100-107
ABSTRACT
This paper presents scalable algorithms and data structures for adaptive mesh refinement computations. We describe a novel mesh restructuring algorithm for adaptive mesh refinement computations that uses a constant number of collectives regardless of the refinement depth. To further increase scalability, we describe a localized hierarchical coordinate-based block indexing scheme in contrast to traditional linear numbering schemes, which incur unnecessary synchronization. In contrast to the existing approaches which take O(P) time and storage per process, our approach takes only constant time and has very small memory footprint. With these optimizations as well as an efficient mapping scheme, our algorithm is scalable and suitable for large, highly-refined meshes. We present strong-scaling experiments up to 2k ranks on Cray XK6, and 32k ranks on IBM Blue Gene/Q.
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CITATION
Akhil Langer, Jonathan Lifflander, Phil Miller, Kuo-Chuan Pan, Laxmikant V. Kale, Paul Ricker, "Scalable Algorithms for Distributed-Memory Adaptive Mesh Refinement", Computer Architecture and High Performance Computing, Symposium on, vol. 00, no. , pp. 100-107, 2012, doi:10.1109/SBAC-PAD.2012.48
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