Scalable Computation of Streamlines on Very Large Datasets

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Understanding vector fields resulting from large scientific simulations is an important and often difficult task. Streamlines, curves that are tangential to a vector field at each point, are a powerful visualization method in this context. Application of streamline-based visualization to very large vector field data represents a significant challenge due to the non-local and data-dependent nature of streamline computation, and requires careful balancing of computational demands placed on I/O, memory, communication, and processors. In this paper we review two parallelization approaches based on established parallelization paradigms (static decomposition and on-demand loading) and present a novel hybrid algorithm for computing streamlines. ... continued below

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Pugmire, David; Childs, Hank; Garth, Christoph; Ahern, Sean & Weber, Gunther H. September 1, 2009.

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Understanding vector fields resulting from large scientific simulations is an important and often difficult task. Streamlines, curves that are tangential to a vector field at each point, are a powerful visualization method in this context. Application of streamline-based visualization to very large vector field data represents a significant challenge due to the non-local and data-dependent nature of streamline computation, and requires careful balancing of computational demands placed on I/O, memory, communication, and processors. In this paper we review two parallelization approaches based on established parallelization paradigms (static decomposition and on-demand loading) and present a novel hybrid algorithm for computing streamlines. Our algorithm is aimed at good scalability and performance across the widely varying computational characteristics of streamline-based problems. We perform performance and scalability studies of all three algorithms on a number of prototypical application problems and demonstrate that our hybrid scheme is able to perform well in different settings.

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  • SuperComputing 2009 (SC09), Portland, Oregon, 11/14/09-11/20/09

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  • Report No.: LBNL-3264E
  • Grant Number: DE-AC02-05CH11231
  • Office of Scientific & Technical Information Report Number: 983119
  • Archival Resource Key: ark:/67531/metadc1014427

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  • September 1, 2009

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  • Oct. 14, 2017, 8:36 a.m.

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  • Oct. 18, 2017, 10:10 a.m.

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Pugmire, David; Childs, Hank; Garth, Christoph; Ahern, Sean & Weber, Gunther H. Scalable Computation of Streamlines on Very Large Datasets, article, September 1, 2009; Berkeley, California. (digital.library.unt.edu/ark:/67531/metadc1014427/: accessed October 16, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.