|Title||Self-Adaptive Density Estimation of Particle Data |
|Publication Type||Journal Article |
|Year of Publication||2015 |
|Authors||Peterka, T, Croubois, H, Li, N, Rangel, S, Cappello, F |
|Journal||SIAM Journal on Scientific Computing |
|Other Numbers||ANL/MCS-P5334-0415 |
|Abstract||We 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.