Feature-based analysis of large-scale spatio-temporal sensor data on hybrid architectures

Authors:
Joel H. Saltz;George Teodoro;Tony Pan;Lee A.D. Cooper;Jun Kong;Scott Klasky;Tahsin M. Kurc
Affiliations:
Center for Comprehensive Informatics and Biomedical Informatics Department, Emory University, USA;Center for Comprehensive Informatics and Biomedical Informatics Department, Emory University, USA;Center for Comprehensive Informatics and Biomedical Informatics Department, Emory University, USA;Center for Comprehensive Informatics and Biomedical Informatics Department, Emory University, USA;Center for Comprehensive Informatics and Biomedical Informatics Department, Emory University, USA;Scientific Data Group, Oak Ridge National Laboratory, USA;Center for Comprehensive Informatics and Biomedical Informatics Department, Emory University, USA, Scientific Data Group, Oak Ridge National Laboratory, USA
Venue:
International Journal of High Performance Computing Applications
Year:
2013

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Abstract

The 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 of large spatio-temporal datasets.