Contour Tree Depth Images For Large Data Visualization

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dc.contributor.authorBiedert, Timen_US
dc.contributor.authorGarth, Christophen_US
dc.contributor.editorC. Dachsbacher and P. Navrátilen_US
dc.date.accessioned2015-05-24T19:41:17Z
dc.date.available2015-05-24T19:41:17Z
dc.date.issued2015en_US
dc.description.abstractHigh-fidelity simulation models on large-scale parallel computer systems can produce data at high computational throughput, but modern architectural trade-offs make full persistent storage to the slow I/O subsystem prohibitively costly with respect to time. We demonstrate the feasibility and potential of combining in situ topological contour tree analysis and compact image-based data representation to address this problem. Our experiments show significant reductions in storage requirements using topology-guided layered depth imaging, while preserving flexibility for explorative visualization and analysis. Our approach represents an effective and easy-to-control trade-off between storage overhead and visualization fidelity for large data visualization.en_US
dc.description.sectionheadersImproved Algorithmsen_US
dc.description.seriesinformationEurographics Symposium on Parallel Graphics and Visualizationen_US
dc.identifier.doi10.2312/pgv.20151158en_US
dc.identifier.pages77-86en_US
dc.identifier.urihttps://doi.org/10.2312/pgv.20151158en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectI.3.6 [Computer Graphics]en_US
dc.subjectMethodology and Techniquesen_US
dc.subjectGraphics data structures and data typesen_US
dc.titleContour Tree Depth Images For Large Data Visualizationen_US
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EGPGV15: Eurographics Symposium on Parallel Graphics and Visualization