Exploring power behaviors and trade-offs of in-situ data analytics

  • Authors:
  • Marc Gamell;Ivan Rodero;Manish Parashar;Janine C. Bennett;Hemanth Kolla;Jacqueline Chen;Peer-Timo Bremer;Aaditya G. Landge;Attila Gyulassy;Patrick McCormick;Scott Pakin;Valerio Pascucci;Scott Klasky

  • Affiliations:
  • Rutgers University;Rutgers University;Rutgers University;Sandia National Laboratories;Sandia National Laboratories;Sandia National Laboratories;Lawrence Livermore National Laboratory & University of Utah;University of Utah;University of Utah;Los Alamos National Laboratory;Los Alamos National Laboratory;University of Utah & Pacific Northwest National Laboratory;Oak Ridge National Laboratory

  • Venue:
  • SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
  • Year:
  • 2013

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Abstract

As scientific applications target exascale, challenges related to data and energy are becoming dominating concerns. For example, coupled simulation workflows are increasingly adopting in-situ data processing and analysis techniques to address costs and overheads due to data movement and I/O. However it is also critical to understand these overheads and associated trade-offs from an energy perspective. The goal of this paper is exploring data-related energy/performance trade-offs for end-to-end simulation workflows running at scale on current high-end computing systems. Specifically, this paper presents: (1) an analysis of the data-related behaviors of a combustion simulation workflow with an in-situ data analytics pipeline, running on the Titan system at ORNL; (2) a power model based on system power and data exchange patterns, which is empirically validated; and (3) the use of the model to characterize the energy behavior of the workflow and to explore energy/performance trade-offs on current as well as emerging systems.