Performance analysis techniques for the exascale co-design process

Martin Schulz, Jim Belak, Abhinav Bhatele, Peer Timo Bremer, Greg Bronevetsky, Marc Casas, Todd Gamblin, Katherine E. Isaacs, Ignacio Laguna, Joshua Levine, Valerio Pascucci, David Richards, Barry Rountree

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Efficient and effective performance analysis techniques are critical for the development of future generation systems. They are the drivers behind the required co-design process that helps establish the principles needed for their design. In this paper, we will highlight two such approaches: PAVE, a project that investigates mapping of performance data to more intuitive domains and uses advanced visualization techniques to expose problems, and GREMLIN, a system evaluation environment capable of emulating expected properties of exascale architectures on petascale machines. Combined with other approaches in system modeling and simulation, these projects enable us to provide a meaningful introspection into a target application's characteristics as well as its expected behavior and, more importantly, likely bottlenecks on future generation machines.

Original languageEnglish (US)
Title of host publicationParallel Computing
Subtitle of host publicationAccelerating Computational Science and Engineering (CSE)
PublisherIOS Press BV
Pages19-32
Number of pages14
ISBN (Print)9781614993803
DOIs
StatePublished - Jan 1 2014
Externally publishedYes

Publication series

NameAdvances in Parallel Computing
Volume25
ISSN (Print)0927-5452

Keywords

  • Architecture Emulation
  • Co-Design
  • Performance Analysis
  • Performance Visualization

ASJC Scopus subject areas

  • Computer Science(all)

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  • Cite this

    Schulz, M., Belak, J., Bhatele, A., Bremer, P. T., Bronevetsky, G., Casas, M., Gamblin, T., Isaacs, K. E., Laguna, I., Levine, J., Pascucci, V., Richards, D., & Rountree, B. (2014). Performance analysis techniques for the exascale co-design process. In Parallel Computing: Accelerating Computational Science and Engineering (CSE) (pp. 19-32). (Advances in Parallel Computing; Vol. 25). IOS Press BV. https://doi.org/10.3233/978-1-61499-381-0-19