RBF Volume Ray Casting on Multicore and Manycore CPUs

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dc.contributor.authorKnoll, Aaronen_US
dc.contributor.authorWald, Ingoen_US
dc.contributor.authorNavratil, Paulen_US
dc.contributor.authorBowen, Anneen_US
dc.contributor.authorReda, Khairien_US
dc.contributor.authorPapka, Mike E.en_US
dc.contributor.authorGaither, Kellyen_US
dc.contributor.editorH. Carr, P. Rheingans, and H. Schumannen_US
dc.date.accessioned2015-03-03T12:34:11Z
dc.date.available2015-03-03T12:34:11Z
dc.date.issued2014en_US
dc.description.abstractModern supercomputers enable increasingly large N-body simulations using unstructured point data. The structures implied by these points can be reconstructed implicitly. Direct volume rendering of radial basis function (RBF) kernels in domain-space offers flexible classification and robust feature reconstruction, but achieving performant RBF volume rendering remains a challenge for existing methods on both CPUs and accelerators. In this paper, we present a fast CPU method for direct volume rendering of particle data with RBF kernels. We propose a novel two-pass algorithm: first sampling the RBF field using coherent bounding hierarchy traversal, then subsequently integrating samples along ray segments. Our approach performs interactively for a range of data sets from molecular dynamics and astrophysics up to 82 million particles. It does not rely on level of detail or subsampling, and offers better reconstruction quality than structured volume rendering of the same data, exhibiting comparable performance and requiring no additional preprocessing or memory footprint other than the BVH. Lastly, our technique enables multi-field, multi-material classification of particle data, providing better insight and analysis.en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.identifier.doi10.1111/cgf.12363en_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/cgf.12363en_US
dc.publisherThe Eurographics Association and John Wiley and Sons Ltd.en_US
dc.titleRBF Volume Ray Casting on Multicore and Manycore CPUsen_US
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EuroVis14: Eurographics Conference on Visualization