Efficient Point Merging Using Data Parallel Techniques

dc.contributor.authorYenpure, Abhisheken_US
dc.contributor.authorChilds, Hanken_US
dc.contributor.authorMoreland, Kennethen_US
dc.contributor.editorChilds, Hank and Frey, Steffenen_US
dc.date.accessioned2019-06-02T18:26:06Z
dc.date.available2019-06-02T18:26:06Z
dc.date.issued2019
dc.description.abstractWe study the problem of merging three-dimensional points that are nearby or coincident. We introduce a fast, efficient approach that uses data parallel techniques for execution in various shared-memory environments. Our technique incorporates a heuristic for efficiently clustering spatially close points together, which is one reason our method performs well against other methods. We then compare our approach against methods of a widely-used scientific visualization library accompanied by a performance study that shows our approach works well with different kinds of parallel hardware (many-core CPUs and NVIDIA GPUs) and data sets of various sizes.en_US
dc.description.sectionheadersSession 3
dc.description.seriesinformationEurographics Symposium on Parallel Graphics and Visualization
dc.identifier.doi10.2312/pgv.20191112
dc.identifier.isbn978-3-03868-079-6
dc.identifier.issn1727-348X
dc.identifier.pages79-88
dc.identifier.urihttps://doi.org/10.2312/pgv.20191112
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/pgv20191112
dc.publisherThe Eurographics Associationen_US
dc.subjectComputing methodologies
dc.subjectShared memory algorithms
dc.subjectScientific visualization
dc.subjectComputer graphics
dc.titleEfficient Point Merging Using Data Parallel Techniquesen_US
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