
Proceedings Paper
Abstract rendering: out-of-core rendering for information visualizationFormat | Member Price | Non-Member Price |
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Paper Abstract
As visualization is applied to larger data sets residing in more diverse hardware environments, visualization frameworks need to adapt. Rendering techniques are currently a major limiter since they tend to be built around central processing with all of the geometric data present. This is not a fundamental requirement of information visualization. This paper presents Abstract Rendering (AR), a technique for eliminating the centralization requirement while preserving some forms of interactivity. AR is based on the observation that pixels are fundamentally bins, and that rendering is essentially a binning process on a lattice of bins. By providing a more flexible binning process, the majority of rendering can be done with the geometric information stored out-of-core. Only the bin representations need to reside in memory. This approach enables: (1) rendering on large datasets without requiring large amounts of working memory, (2) novel and useful control over image composition, (3) a direct means of distributing the rendering task across processes, and (4) high-performance interaction techniques on large datasets. This paper introduces AR in a theoretical context, provides an overview of an implementation, and discusses how it has been applied to large-scale data visualization problems.
Paper Details
Date Published: 3 February 2014
PDF: 13 pages
Proc. SPIE 9017, Visualization and Data Analysis 2014, 90170K (3 February 2014); doi: 10.1117/12.2041200
Published in SPIE Proceedings Vol. 9017:
Visualization and Data Analysis 2014
Pak Chung Wong; David L. Kao; Ming C. Hao; Chaomei Chen, Editor(s)
PDF: 13 pages
Proc. SPIE 9017, Visualization and Data Analysis 2014, 90170K (3 February 2014); doi: 10.1117/12.2041200
Show Author Affiliations
Joseph A. Cottam, Indiana Univ. (United States)
Andrew Lumsdaine, Indiana Univ. (United States)
Andrew Lumsdaine, Indiana Univ. (United States)
Peter Wang, Continuum Analytics (United States)
Published in SPIE Proceedings Vol. 9017:
Visualization and Data Analysis 2014
Pak Chung Wong; David L. Kao; Ming C. Hao; Chaomei Chen, Editor(s)
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