Yenpure, AbhishekChilds, HankMoreland, KennethChilds, Hank and Frey, Steffen2019-06-022019-06-022019978-3-03868-079-61727-348Xhttps://doi.org/10.2312/pgv.20191112https://diglib.eg.org:443/handle/10.2312/pgv20191112We 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.Computing methodologiesShared memory algorithmsScientific visualizationComputer graphics10.2312/pgv.2019111279-88