A Scalable, Hybrid Scheme for Volume Rendering Massive Data Sets

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dc.contributor.authorChilds, Hanken_US
dc.contributor.authorDuchaineau, Marken_US
dc.contributor.authorMa, Kwan-Liuen_US
dc.contributor.editorAlan Heirich and Bruno Raffin and Luis Paulo dos Santosen_US
dc.date.accessioned2014-01-26T16:30:50Z
dc.date.available2014-01-26T16:30:50Z
dc.date.issued2006en_US
dc.description.abstractWe introduce a parallel, distributed memory algorithm for volume rendering massive data sets. The algorithm's scalability has been demonstrated up to 400 processors, rendering one hundred million unstructured elements in under one second. The heart of the algorithm is a hybrid approach that parallelizes over both the elements of the input data and over the pixels of the output image. At each stage of the algorithm, there are strong limits on how much work each processor performs, ensuring good parallel efficiency. The algorithm is sample-based. We present two techniques for calculating the sample points: a 3D rasterization technique and a kernel-based technique, which trade off between speed and generality. Finally, the algorithm is very flexible. It can be deployed in general purpose visualization tools and can also support diverse mesh types, ranging from structured grids to curvilinear and unstructured meshes to point clouds.en_US
dc.description.seriesinformationEurographics Symposium on Parallel Graphics and Visualizationen_US
dc.identifier.isbn3-905673-40-1en_US
dc.identifier.issn1727-348Xen_US
dc.identifier.urihttps://doi.org/10.2312/EGPGV/EGPGV06/153-161en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectCategories and Subject Descriptors (according to ACM CCS): I.3.2 [Computer Graphics]: Distributed/Network Graphics; I.3.3 [Computer Graphics]: Picture/Image Generation; and I.4.1 [Computer Graphics]: Samplingen_US
dc.titleA Scalable, Hybrid Scheme for Volume Rendering Massive Data Setsen_US
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EGPGV06: Eurographics Symposium on Parallel Graphics and Visualization