SELF-ADAPTIVE DENSITY ESTIMATION OF PARTICLE DATA

We present a study of density estimation, the conversion of discrete particle positions to a continuous field of particle density defined over a three-dimensional 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.