Parallel hierarchical visualization of large time-varying 3D vector fields

Hongfeng Yu, Chaoli Wang, Kwan-Liu Ma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

50 Scopus citations

Abstract

We present the design of a scalable parallel pathline construction method for visualizing large time-varying 3D vector fields. A 4D (i.e., time and the 3D spatial domain) representation of the vector field is introduced to make a time-accurate depiction of the flow field. This representation also allows us to obtain pathlines through streamline tracing in the 4D space. Furthermore, a hierarchical representation of the 4D vector field, constructed by clustering the 4D field, makes possible interactive visualization of the flow field at different levels of abstraction. Based on this hierarchical representation, a data partitioning scheme is designed to achieve high parallel efficiency. We demonstrate the performance of parallel pathline visualization using data sets obtained from terascale flow simulations. This new capability will enable scientists to study their time-varying vector fields at the resolution and interactivity previously unavailable to them. (c) 2007 ACM.

Original languageEnglish (US)
Title of host publicationProceedings of the 2007 ACM/IEEE Conference on Supercomputing, SC'07
DOIs
StatePublished - Dec 1 2007
Event2007 ACM/IEEE Conference on Supercomputing, SC'07 - Reno, NV, United States
Duration: Nov 10 2007Nov 16 2007

Other

Other2007 ACM/IEEE Conference on Supercomputing, SC'07
CountryUnited States
CityReno, NV
Period11/10/0711/16/07

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Parallel hierarchical visualization of large time-varying 3D vector fields'. Together they form a unique fingerprint.

  • Cite this

    Yu, H., Wang, C., & Ma, K-L. (2007). Parallel hierarchical visualization of large time-varying 3D vector fields. In Proceedings of the 2007 ACM/IEEE Conference on Supercomputing, SC'07 [24] https://doi.org/10.1145/1362622.1362655