Hybrid Data-Parallel Contour Tree Computation
| dc.contributor.author | Carr, Hamish | en_US |
| dc.contributor.author | Sewell, Christopher | en_US |
| dc.contributor.author | Lo, Li-Ta | en_US |
| dc.contributor.author | Ahrens, James | en_US |
| dc.contributor.editor | Cagatay Turkay and Tao Ruan Wan | en_US |
| dc.date.accessioned | 2016-09-15T09:05:53Z | |
| dc.date.available | 2016-09-15T09:05:53Z | |
| dc.date.issued | 2016 | |
| dc.description.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 computation 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. | en_US |
| dc.description.sectionheaders | Geometry and Surfaces | |
| dc.description.seriesinformation | Computer Graphics and Visual Computing (CGVC) | |
| dc.identifier.doi | 10.2312/cgvc.20161299 | |
| dc.identifier.isbn | 978-3-03868-022-2 | |
| dc.identifier.issn | - | |
| dc.identifier.pages | 73-80 | |
| dc.identifier.uri | https://doi.org/10.2312/cgvc.20161299 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/cgvc20161299 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.title | Hybrid Data-Parallel Contour Tree Computation | en_US |
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