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Title: A Parallel Ghosting Algorithm for The Flexible Distributed Mesh Database

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 datamore » to carry out computation.« less

Authors:
 [1];  [1];  [1];  [1]
  1. Scientific Computation Research Center, Rensselaer Polytechnic Institute, 110 8th St, Troy, NY 12180, USA
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1197889
Grant/Contract Number:  
FC02-06ER25769
Resource Type:
Published Article
Journal Name:
Scientific Programming
Additional Journal Information:
Journal Name: Scientific Programming Journal Volume: 21 Journal Issue: 1-2; Journal ID: ISSN 1058-9244
Publisher:
Hindawi Publishing Corporation
Country of Publication:
Egypt
Language:
English

Citation Formats

Mubarak, Misbah, Seol, Seegyoung, Lu, Qiukai, and Shephard, Mark S. A Parallel Ghosting Algorithm for The Flexible Distributed Mesh Database. Egypt: N. p., 2013. Web. doi:10.1155/2013/654971.
Mubarak, Misbah, Seol, Seegyoung, Lu, Qiukai, & Shephard, Mark S. A Parallel Ghosting Algorithm for The Flexible Distributed Mesh Database. Egypt. doi:10.1155/2013/654971.
Mubarak, Misbah, Seol, Seegyoung, Lu, Qiukai, and Shephard, Mark S. Tue . "A Parallel Ghosting Algorithm for The Flexible Distributed Mesh Database". Egypt. doi:10.1155/2013/654971.
@article{osti_1197889,
title = {A Parallel Ghosting Algorithm for The Flexible Distributed Mesh Database},
author = {Mubarak, Misbah and Seol, Seegyoung and Lu, Qiukai and Shephard, Mark S.},
abstractNote = {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.},
doi = {10.1155/2013/654971},
journal = {Scientific Programming},
number = 1-2,
volume = 21,
place = {Egypt},
year = {2013},
month = {1}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1155/2013/654971

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