Novel views of performance data to analyze large-scale adaptive applications

  • Authors:

  • Abhinav Bhatele;Todd Gamblin;Katherine E. Isaacs;Brian T. N. Gunney;Martin Schulz;Peer-Timo Bremer;Bernd Hamann

  • Affiliations:

  • Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, CA;Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, CA;University of California, Davis, CA;Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, CA;Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, CA;Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, CA;University of California, Davis, CA

  • Venue:
  • SC '12 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
  • Year:
  • 2012

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Abstract

Performance analysis of parallel scientific codes is becoming increasingly difficult due to the rapidly growing complexity of applications and architectures. Existing tools fall short in providing intuitive views that facilitate the process of performance debugging and tuning. In this paper, we extend recent ideas of projecting and visualizing performance data for faster, more intuitive analysis of applications. We collect detailed per-level and per-phase measurements for a dynamically load-balanced, structured AMR library and project per-core data collected in the hardware domain on to the application's communication topology. We show how our projections and visualizations lead to a rapid diagnosis of and mitigation strategy for a previously elusive scaling bottleneck in the library that is hard to detect using conventional tools. Our new insights have resulted in a 22% performance improvement for a 65,536-core run of the AMR library on an IBM Blue Gene/P system.