Scalable Parallel Distance Field Construction for Large-Scale Applications

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. Anew distri...

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Veröffentlicht in:IEEE transactions on visualization and computer graphics Jg. 21; H. 10; S. 1187 - 1200
Hauptverfasser: Hongfeng Yu, Jinrong Xie, Kwan-Liu Ma, Kolla, Hemanth, Chen, Jacqueline H.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.10.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Computational modeling
Data models
distance field
geometric modeling
in-situ processing
large-scale scientific data analytics and visualization
MATHEMATICS AND COMPUTING
Octrees
parallel algorithms
Program processors
scalability
scientific simulations
spatial data structures
Surface treatment
Three-dimensional displays
parallel algorithms
geometric modeling
scientific simulations
spatial data structures
large-scale scientific data analytics and visualization
Distance field
in-situ processing
scalability
ISSN:1077-2626, 1941-0506
Online-Zugang:Volltext
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Zusammenfassung: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. Anew distributed spatial data structure, named parallel distance tree, is introduced to manage the level sets of data and facilitate surface tracking overtime, 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.
Bibliographie:ObjectType-Article-1
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SAND-2016-6038J
AC04-94AL85000
USDOE National Nuclear Security Administration (NNSA)
ISSN:1077-2626
1941-0506
DOI:10.1109/TVCG.2015.2417572