SC Conference - Activity Details

Capturing Performance Knowledge for Automated Analysis

Kevin A. Huck  (University of Oregon)
Oscar Hernandez  (University of Houston)
Van Bui  (University of Houston)
Barbara Chapman  (University of Houston)
Allen D. Malony  (University of Oregon)
Boyana Norris  (Argonne National Laboratory)
Sunita Chandrasekaran  (Nanyang Technological University)
Lois Curfman McInnes  (Argonne National Laboratory)
Papers Session
Performance Tools
Thursday,  02:00PM - 02:30PM
Room Ballroom E
Automating the process of parallel performance experimentation, analysis, and problem diagnosis can enhance environments for performance-directed application development, compilation, and execution. This is especially true when parametric studies, modeling, and optimization strategies require large amounts of data to be collected and processed for knowledge synthesis and reuse. This paper describes the integration of the PerfExplorer performance data mining framework with the OpenUH compiler infrastructure. OpenUH provides auto-instrumentation of source code for performance experimentation and PerfExplorer provides automated and reusable analysis of the performance data through a scripting interface. More importantly, PerfExplorer inference rules have been developed to recognize and diagnose performance characteristics important for optimization strategies and modeling. Three case studies are presented which show our success with automation in OpenMP and MPI code tuning, parametric characterization, and power modeling. The paper discusses how the integration supports performance knowledge engineering across applications and feedback-based compiler optimization in general.
The full paper can be found in the IEEE Xplore Digital Library and ACM Digital Library
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