Hybrid Data-Parallel Contour Tree Computation

dc.contributor.authorCarr, Hamishen_US
dc.contributor.authorSewell, Christopheren_US
dc.contributor.authorLo, Li-Taen_US
dc.contributor.authorAhrens, Jamesen_US
dc.contributor.editorCagatay Turkay and Tao Ruan Wanen_US
dc.date.accessioned2016-09-15T09:05:53Z
dc.date.available2016-09-15T09:05:53Z
dc.date.issued2016
dc.description.abstractAs data sets increase in size beyond the petabyte, it is increasingly important to have automated methods for data analysis and visualisation. While topological analysis tools such as the contour tree and Morse-Smale complex are now well established, there is still a shortage of efficient parallel algorithms for their computation, in particular for massively data-parallel computation on a SIMD model. We report the first data-parallel algorithm for computing the fully augmented contour tree, using a quantised computation model. We then extend this to provide a hybrid data-parallel / distributed algorithm allowing scaling beyond a single GPU or CPU, and provide results for its computation. Our implementation uses the portable data-parallel primitives provided by NVIDIA's Thrust library, allowing us to compile our same code for both GPUs and multi-core CPUs.en_US
dc.description.sectionheadersGeometry and Surfaces
dc.description.seriesinformationComputer Graphics and Visual Computing (CGVC)
dc.identifier.doi10.2312/cgvc.20161299
dc.identifier.isbn978-3-03868-022-2
dc.identifier.issn-
dc.identifier.pages73-80
dc.identifier.urihttps://doi.org/10.2312/cgvc.20161299
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/cgvc20161299
dc.publisherThe Eurographics Associationen_US
dc.titleHybrid Data-Parallel Contour Tree Computationen_US
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