Year: 2013
Type: article
Abstract: 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.
Institutions: Emory University, Oak Ridge National Laboratory
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funders: National Science Foundation, National Institutes of Health, National Heart, Lung, and Blood Institute, National Cancer Institute, National Institute of Biomedical Imaging and Bioengineering, U.S. National Library of Medicine, National Center for Advancing Translational Sciences, National Institute on Minority Health and Health Disparities
Awards: G1959626825, G2036193824, Image Mining for Comparative Analysis of Expression Patterns in Tissue Microarray, Keeneland: National Institute for Experimental Computing, G3694745470, Minority Health-GRID Network: A Genomics Resource for Health Disparity Research, G386607036, G4033445783, G5888954722, G661928818, Center for Grid-Enabled Medical Image Analysis
OpenAlex ID: W2040285774