Scalable Parallel Distance Field Construction for Large-Scale Applications

Hongfeng Yu, Jinrong Xie, Kwan Liu Ma, Hemanth Kolla, Jacqueline H. Chen

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


Computing distance fields is fundamental to many scientific and engineering applications. Distance fields can be used to direct analysis and reduce data. In this paper, we present a highly scalable method for computing 3D distance fields on massively parallel distributed-memory 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 real-world applications. We demonstrate its efficiency and scalability on state-of-the-art supercomputers using both large-scale volume datasets 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.

Original languageEnglish (US)
Article number7072474
Pages (from-to)1187-1200
Number of pages14
JournalIEEE Transactions on Visualization and Computer Graphics
Issue number10
StatePublished - Oct 1 2015


  • distance field
  • geometric modeling
  • in-situ processing
  • large-scale scientific data analytics and visualization
  • parallel algorithms
  • scalability
  • scientific simulations
  • spatial data structures

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design


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