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SC Conference - Activity Details
An Analysis Framework for Performance Data Mining and Knowledge-driven Performance Analysis
Presenter:
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Kevin Huck
(University of Oregon)
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Doctoral Research Showcase Session
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Thursday, 04:30PM - 04:45PM
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Room 17A/17B
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Abstract:
Parallel performance diagnosis and engineering requires more sophisticated
support for analysis automation, knowledge integration, intelligent problem
discovery, and prescriptive feedback. How such capabilities are designed and
implemented in a performance analysis framework includes consideration for
programmability, extensibility, interoperability, and reuse. At the dawn of
the petascale age, issues of scalability are also a serious concern both when
analyzing the performance of thousands of cores and managing parametric studies
with hundreds or thousands of experiments.
Our approach to these complex analysis challenges is the design and
development of PerfExplorer, a knowledge-engineering, rule-based, performance
data mining framework. PerfExplorer provides dimension reduction, clustering,
correlation and comparative analysis of parallel performance profiles.
PerfExplorer also includes infrastructure for data persistence, provenance,
and a scripting interface and inference engine to capture performance expert
reasoning and build an analysis knowledge base for intelligent problem
solving. PerfExplorer is distributed as part of the TAU performance system.
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