FunM2C: A Filter for Uncertainty Visualization of Multivariate Data on Multi-Core Devices

Gautam Hari, Nrushad Joshi, Zhe Wang, Qian Gong, Dave Pugmire, Kenneth Moreland, Chris R. Johnson, Scott Klasky, Norbert Podhorszki, Tushar M. Athawale

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Uncertainty visualization is an emerging research topic in data visualization because neglecting uncertainty in visualization can lead to inaccurate assessments. In this paper, we study the propagation of multivariate data uncertainty in visualization. Although there have been a few advancements in probabilistic uncertainty visualization of multivariate data, three critical challenges remain to be addressed. First, the state-of-the-art probabilistic uncertainty visualization framework is limited to bivariate data (two variables). Second, existing uncertainty visualization algorithms use computationally intensive techniques and lack support for cross-platform portability. Third, as a consequence of the computational expense, integration into production visualization tools is impractical. In this work, we address all three issues and make a threefold contribution. First, we take a step to generalize the state-of-the-art probabilistic framework for bivariate data to multivariate data with an arbitrary number of variables. Second, through utilization of VTK-m's shared-memory parallelism and cross-platform compatibility features, we demonstrate acceleration of multivariate uncertainty visualization on different many-core architectures, including OpenMP and AMD GPUs. Third, we demonstrate the integration of our algorithms with the ParaView software. We demonstrate the utility of our algorithms through experiments on multivariate simulation data with three and four variables.

Original languageEnglish
Title of host publicationProceedings - 2024 IEEE Workshop on Uncertainty Visualization
Subtitle of host publicationApplications, Techniques, Software, and Decision Frameworks, UncertaintyVis 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages43-47
Number of pages5
ISBN (Electronic)9798331527600
DOIs
StatePublished - 2024
Event2024 IEEE Workshop on Uncertainty Visualization: Applications, Techniques, Software, and Decision Frameworks, UncertaintyVis 2024 - St. Pete Beach, United States
Duration: Oct 14 2024 → …

Publication series

NameProceedings - 2024 IEEE Workshop on Uncertainty Visualization: Applications, Techniques, Software, and Decision Frameworks, UncertaintyVis 2024

Conference

Conference2024 IEEE Workshop on Uncertainty Visualization: Applications, Techniques, Software, and Decision Frameworks, UncertaintyVis 2024
Country/TerritoryUnited States
CitySt. Pete Beach
Period10/14/24 → …

Funding

This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a non-exclusive, paid up, irrevocable, world-wide license to publish or reproduce the published form of the manuscript, or allow others to do so, for U.S. Government purposes.

Keywords

  • multivariate visualization
  • Uncertainty

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