Carr, H, Sewell, C, Lo, L-T et al. (1 more author) (2016) Hybrid Data-Parallel Contour Tree Computation. In: Turkay, C and Wan, TR, (eds.) Computer Graphics & Visual Computing. CGVC 2016, 15-16 Sep 2016, Bournemouth, UK. The Eurographics Association ISBN 978-3-03868-022-2
Abstract
As data sets increase in size beyond the petabyte, it is increasingly important to have automated methods for data analysis and visualisation. While topological analysis tools such as the contour tree and Morse-Smale complex are now well established, there is still a shortage of efficient parallel algorithms for their computation, in particular for massively data-parallel compu- tation on a SIMD model. We report the first data-parallel algorithm for computing the fully augmented contour tree, using a quantised computation model. We then extend this to provide a hybrid data-parallel / distributed algorithm allowing scaling beyond a single GPU or CPU, and provide results for its computation. Our implementation uses the portable data-parallel primitives provided by NVIDIA’s Thrust library, allowing us to compile our same code for both GPUs and multi-core CPUs.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Keywords: | topological analysis, contour tree, merge tree, data parallel algorithms |
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) > Institute for Computational and Systems Science (Leeds) |
Funding Information: | Funder Grant number EPSRC EP/J013072/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 09 Nov 2016 14:55 |
Last Modified: | 15 Aug 2017 08:57 |
Published Version: | https://doi.org/10.2312/cgvc.20161299 |
Status: | Published |
Publisher: | The Eurographics Association |
Identification Number: | 10.2312/cgvc.20161299 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:107190 |