Scalable Critical-Path Based Performance Analysis

The critical path, which describes the longest execution sequence without wait states in a parallel program, identifies the activities that determine the overall program runtime. Combining knowledge of the critical path with traditional parallel profiles, we have defined a set of compact performance...

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Veröffentlicht in:2012 IEEE 26th International Parallel and Distributed Processing Symposium S. 1330 - 1340
Hauptverfasser: Bohme, D., Wolf, F., de Supinski, B. R., Schulz, M., Geimer, M.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.05.2012
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ISBN:1467309753, 9781467309752
ISSN:1530-2075
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Abstract The critical path, which describes the longest execution sequence without wait states in a parallel program, identifies the activities that determine the overall program runtime. Combining knowledge of the critical path with traditional parallel profiles, we have defined a set of compact performance indicators that help answer a variety of important performance-analysis questions, such as identifying load imbalance, quantifying the impact of imbalance on runtime, and characterizing resource consumption. By replaying event traces in parallel, we can calculate these performance indicators in a highly scalable way, making them a suitable analysis instrument for massively parallel programs with thousands of processes. Case studies with real-world parallel applications confirm that - in comparison to traditional profiles - our indicators provide enhanced insight into program behavior, especially when evaluating partitioning schemes of MPMD programs.
AbstractList The critical path, which describes the longest execution sequence without wait states in a parallel program, identifies the activities that determine the overall program runtime. Combining knowledge of the critical path with traditional parallel profiles, we have defined a set of compact performance indicators that help answer a variety of important performance-analysis questions, such as identifying load imbalance, quantifying the impact of imbalance on runtime, and characterizing resource consumption. By replaying event traces in parallel, we can calculate these performance indicators in a highly scalable way, making them a suitable analysis instrument for massively parallel programs with thousands of processes. Case studies with real-world parallel applications confirm that - in comparison to traditional profiles - our indicators provide enhanced insight into program behavior, especially when evaluating partitioning schemes of MPMD programs.
Author Schulz, M.
Geimer, M.
Wolf, F.
Bohme, D.
de Supinski, B. R.
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  givenname: D.
  surname: Bohme
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  organization: German Res. Sch. for Simulation Sci., Aachen, Germany
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  givenname: F.
  surname: Wolf
  fullname: Wolf, F.
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  givenname: B. R.
  surname: de Supinski
  fullname: de Supinski, B. R.
  email: bronis@llnl.gov
  organization: Lawrence Livermore Nat. Lab., Livermore, CA, USA
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  givenname: M.
  surname: Schulz
  fullname: Schulz, M.
  email: schulzm@llnl.gov
  organization: Lawrence Livermore Nat. Lab., Livermore, CA, USA
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  givenname: M.
  surname: Geimer
  fullname: Geimer, M.
  email: m.geimer@fz-juelich.de
  organization: Julich Supercomput. Centre, Julich, Germany
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Snippet The critical path, which describes the longest execution sequence without wait states in a parallel program, identifies the activities that determine the...
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StartPage 1330
SubjectTerms Analytical models
Optimization
Performance analysis
Resource management
Runtime
Synchronization
Title Scalable Critical-Path Based Performance Analysis
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