SCHEDULE: NOV 10-16, 2012
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Novel Views of Performance Data to Analyze Large-Scale Adaptive Applications
SESSION: Auto-Diagnosis of Correctness and Performance Issues
EVENT TYPE: Papers
TIME: 4:30PM - 5:00PM
SESSION CHAIR: Kenjiro Taura
AUTHOR(S):Abhinav Bhatele, Todd Gamblin, Katherine E. Isaacs, Brian T. N. Gunney, Martin Schulz, Peer-Timo Bremer, Bernd Hamann
ROOM:255-BC
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 in a dynamically load-balanced, structured AMR library and relate the information back to the application's communication structure. We show how our projections and visualizations lead to a simple 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 on an IBM Blue Gene/P system.
Chair/Author Details:
Kenjiro Taura (Chair) - University of Tokyo
Abhinav Bhatele - Lawrence Livermore National Laboratory
Todd Gamblin - Lawrence Livermore National Laboratory
Katherine E. Isaacs - University of California, Davis
Brian T. N. Gunney - Lawrence Livermore National Laboratory
Martin Schulz - Lawrence Livermore National Laboratory
Peer-Timo Bremer - Lawrence Livermore National Laboratory
Bernd Hamann - University of California, Davis
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Novel Views of Performance Data to Analyze Large-Scale Adaptive Applications
SESSION: Auto-Diagnosis of Correctness and Performance Issues
EVENT TYPE:
TIME: 4:30PM - 5:00PM
SESSION CHAIR: Kenjiro Taura
AUTHOR(S):Abhinav Bhatele, Todd Gamblin, Katherine E. Isaacs, Brian T. N. Gunney, Martin Schulz, Peer-Timo Bremer, Bernd Hamann
ROOM:255-BC
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 in a dynamically load-balanced, structured AMR library and relate the information back to the application's communication structure. We show how our projections and visualizations lead to a simple 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 on an IBM Blue Gene/P system.
Chair/Author Details:
Kenjiro Taura (Chair) - University of Tokyo
Abhinav Bhatele - Lawrence Livermore National Laboratory
Todd Gamblin - Lawrence Livermore National Laboratory
Katherine E. Isaacs - University of California, Davis
Brian T. N. Gunney - Lawrence Livermore National Laboratory
Martin Schulz - Lawrence Livermore National Laboratory
Peer-Timo Bremer - Lawrence Livermore National Laboratory
Bernd Hamann - University of California, Davis
Click here to download .ics calendar file