[TVCG paper presentation]
Authors: Hongfeng Yu, Jinrong Xie, Kwan-Liu Ma, Hemanth Kolla, Jacqueline H. Chen
Abstract: Computing distance fields is fundamental to many scientific and engineering applications. Distancefields can be used to direct analysis and reduce data. In this paper, we present a highly scalablemethod for computing 3D distance fields on massively parallel distributedmemory machines. A new distributed spatial data structure, named parallel distance tree, is introduced to manage the level sets of data and facilitate surface tracking over time, resulting in significantly reduced computation and communication costs for calculating the distance to the surface of interest from any spatial locations. Our method supports several data types and distance metrics from realworld applications. We demonstrate its efficiency and scalability on state-of-the-art supercomputers using both large-scalevolume data sets and surface models. We also demonstrate in-situ distance field computation on dynamic turbulent flame surfaces for a petascale combustion simulation. Our work greatly extends the usability of distance fields for demanding applications.