SC Conference - Activity Details
Capturing Performance Knowledge for Automated Analysis
Authors:| 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) |
| 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
| IEEE Computer Society / ACM | 2 0 Y E A R S - U N L E A S H I N G T H E P O W E R O F H P C |