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...

Full description

Saved in:
Bibliographic Details
Published inThe international journal of high performance computing applications Vol. 27; no. 3; p. 263
Main Authors Saltz, Joel, Teodoro, George, Pan, Tony, Cooper, Lee, Kong, Jun, Klasky, Scott, Kurc, Tahsin
Format Journal Article
LanguageEnglish
Published United States 01.08.2013
Online AccessGet more information

Cover

More Information
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.
ISSN:1094-3420
DOI:10.1177/1094342013488260