Feature-based Analysis of Large-scale Spatio-Temporal Sensor Data on Hybrid Architectures
Analysis of large sensor datasets for structural and functional features has applications in many domains, including weather and climate modeling, characterization of subsurface reservoirs, and biomedicine. The vast amount of data obtained from state-of-the-art sensors and the computational cost of...
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Published in | The international journal of high performance computing applications Vol. 27; no. 3; p. 263 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
01.08.2013
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Online Access | Get more information |
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Summary: | Analysis of large sensor datasets for structural and functional features has applications in many domains, including weather and climate modeling, characterization of subsurface reservoirs, and biomedicine. The vast amount of data obtained from state-of-the-art sensors and the computational cost of analysis operations create a barrier to such analyses. In this paper, we describe middleware system support to take advantage of large clusters of hybrid CPU-GPU nodes to address the data and compute-intensive requirements of feature-based analyses in large spatio-temporal datasets. |
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ISSN: | 1094-3420 |
DOI: | 10.1177/1094342013488260 |