Hristov, P, Weber, G, Carr, H orcid.org/0000-0001-6739-0283 et al. (2 more authors) (2020) Data Parallel Hypersweeps for in Situ Topological Analysis. In: Proceedings of2020 IEEE 10th Symposium on Large Data Analysis and Visualization (LDAV). 2020 IEEE 10th Symposium on Large Data Analysis and Visualization (LDAV), 25 Oct 2020, Salt Lake City, USA. IEEE , pp. 12-21. ISBN 978-1-7281-8468-5
Abstract
The contour tree is a tool for understanding the topological structure of a scalar field. Recent work has built efficient contour tree algorithms for shared memory parallel computation, driven by the need to analyze large data sets in situ while the simulation is running. Unfortunately, methods for using the contour tree for practical data analysis are still primarily serial, including single isocontour extraction, branch decomposition and simplification. We report data parallel methods for these tasks using a data structure called the hyperstructure and a general purpose approach called a hypersweep. We implement and integrate these methods with a Cinema database that stores features as depth images and with a web server that reconstructs the features for direct visualization.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | contour tree, in situ, scalar field, geometric measures, branch decomposition |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 12 Nov 2020 12:56 |
Last Modified: | 07 Aug 2023 13:59 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/LDAV51489.2020.00008 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:167115 |