A Parallel Ghosting Algorithm for The Flexible Distributed Mesh DatabaseReport as inadecuate




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Scientific Programming - Volume 21 2013, Issue 1-2, Pages 17-42

Scientific Computation Research Center, Rensselaer Polytechnic Institute, 110 8th St, Troy, NY 12180, USA



Copyright © 2013 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Critical to the scalability of parallel adaptive simulations are parallel control functions including load balancing, reduced inter-process communication and optimal data decomposition. In distributed meshes, many mesh-based applications frequently access neighborhood information for computational purposes which must be transmitted efficiently to avoid parallel performance degradation when the neighbors are on different processors. This article presents a parallel algorithm of creating and deleting data copies, referred to as ghost copies, which localize neighborhood data for computation purposes while minimizing inter-process communication. The key characteristics of the algorithm are: 1 It can create ghost copies of any permissible topological order in a 1D, 2D or 3D mesh based on selected adjacencies. 2 It exploits neighborhood communication patterns during the ghost creation process thus eliminating all-to-all communication. 3 For applications that need neighbors of neighbors, the algorithm can create n number of ghost layers up to a point where the whole partitioned mesh can be ghosted. Strong and weak scaling results are presented for the IBM BG-P and Cray XE6 architectures up to a core count of 32,768 processors. The algorithm also leads to scalable results when used in a parallel super-convergent patch recovery error estimator, an application that frequently accesses neighborhood data to carry out computation.





Author: Misbah Mubarak, Seegyoung Seol, Qiukai Lu, and Mark S. Shephard

Source: https://www.hindawi.com/



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