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Parallel dynamic load-balancing for adaptive unstructured meshes

Parallel dynamic load-balancing for adaptive unstructured meshes

Walshaw, C. ORCID: 0000-0003-0253-7779, Cross, M. and Everett, M. (1998) Parallel dynamic load-balancing for adaptive unstructured meshes. In: Parallel Computational Fluid Dynamics 1997: Recent Developments and Advances Using Parallel Computers. Elsevier B.V., pp. 89-96. ISBN 978-0-444-82849-1 (doi:10.1016/B978-044482849-1/50012-9)

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Abstract

This chapter describes a parallel optimization technique that incorporates a distributed load-balancing algorithm and provides an extremely fast solution to the problem of load-balancing adaptive unstructured meshes. Moreover, a parallel graph contraction technique can be employed to enhance the partition quality and the resulting strategy outperforms or matches results from existing state-of-the-art static mesh partitioning algorithms. The strategy can also be applied to static partitioning problems. Dynamic procedures have been found to be much faster than static techniques, to provide partitions of similar or higher quality and, in comparison, involve the migration of a fraction of the data. The method employs a new iterative optimization technique that balances the workload and attempts to minimize the interprocessor communications overhead. Experiments on a series of adaptively refined meshes indicate that the algorithm provides partitions of an equivalent or higher quality to static partitioners (which do not reuse the existing partition) and much more quickly. The dynamic evolution of load has three major influences on possible partitioning techniques; cost, reuse, and parallelism. The unstructured mesh may be modified every few time-steps and so the load-balancing must have a low cost relative to that of the solution algorithm in between remeshing.

Item Type: Conference Proceedings
Title of Proceedings: Parallel Computational Fluid Dynamics 1997: Recent Developments and Advances Using Parallel Computers
Pre-2014 Departments: School of Computing & Mathematical Sciences
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Last Modified: 14 Oct 2016 09:00
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/294

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