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Proceedings Paper

Subsampling-based compression and flow visualization
Author(s): Alexy Agranovsky; David Camp; Kenneth I. Joy; Hank Childs
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Paper Abstract

As computational capabilities increasingly outpace disk speeds on leading supercomputers, scientists will, in turn, be increasingly unable to save their simulation data at its native resolution. One solution to this problem is to compress these data sets as they are generated and visualize the compressed results afterwards. We explore this approach, specifically subsampling velocity data and the resulting errors for particle advection-based flow visualization. We compare three techniques: random selection of subsamples, selection at regular locations corresponding to multi-resolution reduction, and introduce a novel technique for informed selection of subsamples. Furthermore, we explore an adaptive system which exchanges the subsampling budget over parallel tasks, to ensure that subsampling occurs at the highest rate in the areas that need it most. We perform supercomputing runs to measure the effectiveness of the selection and adaptation techniques. Overall, we find that adaptation is very effective, and, among selection techniques, our informed selection provides the most accurate results, followed by the multi-resolution selection, and with the worst accuracy coming from random subsamples.

Paper Details

Date Published: 8 February 2015
PDF: 14 pages
Proc. SPIE 9397, Visualization and Data Analysis 2015, 93970J (8 February 2015); doi: 10.1117/12.2083251
Show Author Affiliations
Alexy Agranovsky, Univ. of California, Davis (United States)
Lawrence Berkeley National Lab. (United States)
David Camp, Lawrence Berkeley National Lab. (United States)
Kenneth I. Joy, Univ. of California, Davis (United States)
Hank Childs, Lawrence Berkeley National Lab. (United States)
Univ. of Oregon (United States)


Published in SPIE Proceedings Vol. 9397:
Visualization and Data Analysis 2015
David L. Kao; Ming C. Hao; Mark A. Livingston; Thomas Wischgoll, Editor(s)

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