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Proceedings Paper

Visualization of dynamic adaptive resolution scientific data
Author(s): Andrew Foulks; R. Daniel Bergeron; Samuel H. Vohr
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Paper Abstract

Interactive visualization of very large data sets remains a challenging problem to the visualization community. One promising solution involves using adaptive resolution representations of the data. In this model, important regions of data are identified using reconstructive error analysis and are shown in higher detail. During the visualization, regions with higher error are rendered with high resolution data, while areas of low error are rendered at a lower resolution. We have developed a new dynamic adaptive resolution rendering algorithm along with software support libraries. These libraries are designed to extend the VisIt visualization environment by adding support for adaptive resolution data. VisIt supports domain decomposition of data, which we use to define our AR representation. We show that with this model, we achieve performance gains while maintaining error tolerances specified by the scientist.

Paper Details

Date Published: 24 January 2011
PDF: 12 pages
Proc. SPIE 7868, Visualization and Data Analysis 2011, 78680E (24 January 2011); doi: 10.1117/12.873025
Show Author Affiliations
Andrew Foulks, The Univ. of New Hampshire (United States)
R. Daniel Bergeron, The Univ. of New Hampshire (United States)
Samuel H. Vohr, The Univ. of New Hampshire (United States)

Published in SPIE Proceedings Vol. 7868:
Visualization and Data Analysis 2011
Pak Chung Wong; Jinah Park; Ming C. Hao; Chaomei Chen; Katy Börner; David L. Kao; Jonathan C. Roberts, Editor(s)

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