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

Joel H. Saltz*, George Teodoro, Tony Pan, Lee Alex Donald Cooper, Jun Kong, Scott Klasky, Tahsin M. Kurc

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)263-272
Number of pages10
JournalInternational Journal of High Performance Computing Applications
Volume27
Issue number3
DOIs
StatePublished - Aug 2013

Keywords

  • GPGPU
  • Sensor data
  • cluster computing
  • data analysis and management
  • imaging data

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture

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