Document Detail


Extreme scaling of production visualization software on diverse architectures.
MedLine Citation:
PMID:  20650715     Owner:  NLM     Status:  PubMed-not-MEDLINE    
Abstract/OtherAbstract:
This article presents the results of experiments studying how the pure-parallelism paradigm scales to massive data sets, including 16,000 or more cores on trillion-cell meshes, the largest data sets published to date in the visualization literature. The findings on scaling characteristics and bottlenecks contribute to understanding how pure parallelism will perform in the future.
Authors:
Hank Childs; David Pugmire; Sean Ahern; Brad Whitlock; Mark Howison; Gunther H Weber; E Wes Bethel
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Publication Detail:
Type:  Journal Article; Research Support, U.S. Gov't, Non-P.H.S.    
Journal Detail:
Title:  IEEE computer graphics and applications     Volume:  30     ISSN:  1558-1756     ISO Abbreviation:  IEEE Comput Graph Appl     Publication Date:    2010 May-Jun
Date Detail:
Created Date:  2010-07-23     Completed Date:  2010-10-21     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9881869     Medline TA:  IEEE Comput Graph Appl     Country:  United States    
Other Details:
Languages:  eng     Pagination:  22-31     Citation Subset:  -    
Affiliation:
Lawrence Berkeley National Laboratory, and University of California at Davis, CA, USA. hchilds@lbl.gov
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