Argonne National Laboratory

Self-Adaptive Density Estimation of Particle Data

TitleSelf-Adaptive Density Estimation of Particle Data
Publication TypeJournal Article
Year of Publication2015
AuthorsPeterka, T, Croubois, H, Li, N, Rangel, S, Cappello, F
JournalSIAM Journal on Scientific Computing
Other NumbersANL/MCS-P5334-0415
AbstractWe present a study of density estimation, the conversion of discrete particle positions to a continuous field of particle density defined over a 3D Cartesian grid. The study features a methodology for evaluating the accuracy and performance of various density estimation methods, results of that evaluation for four density estimators, and a large-scale parallel algorithm for a self-adaptive method that computes a Voronoi tessellation as an intermediate step. We demonstrate the performance and scalability of our parallel algorithm on a supercomputer when estimating the density of 100 million particles over 500 billion grid points.  
PDFhttp://www.mcs.anl.gov/papers/P5334-0415.pdf