Self-Adaptive Density Estimation of Particle Data

Tom Peterka (1) , Hadrien Croubois (2) , Nan Li (3) , Esteban Rangel , Franck Cappello (4)

1 MCS - Mathematics and Computer Science Division [ANL]
2 ENS de Lyon - École normale supérieure de Lyon
3 University of Chicago
4 JLPC - Joint Laboratory for Petascale Computing [Illinois]

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.

Mots clés: Density estimation; cloud in cell; smoothed particle hydrodynamics; Voronoi tessellation; nearest; grid point; triangular shaped clouds

Domaines: Informatique [cs]; Calcul parallèle, distribué et partagé [cs.DC]

Tom Peterka, Hadrien Croubois, Nan Li, Esteban Rangel, Franck Cappello. Self-Adaptive Density Estimation of Particle Data. SIAM Journal on Scientific Computing, 2016, 38 (5), pp.S646 - S666. ⟨10.1137/15M1016308⟩. ⟨hal-01824023⟩