SCHEDULE: NOV 16-22, 2013

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A Data-Centric Profiler for Parallel Programs

SESSION: Parallel Performance Tools

EVENT TYPE: Papers

TIME: 1:30PM - 2:00PM

SESSION CHAIR: Shirley Moore

AUTHOR(S):Xu Liu, John Mellor-Crummey

ROOM:405/406/407

ABSTRACT:
It is difficult to manually identify opportunities for enhancing data locality. To address this problem, we extended the HPCToolkit performance tools to support data-centric profiling of scalable parallel programs. Our tool uses hardware counters to directly measure memory access latency and attributes latency metrics to both variables and instructions. Different hardware counters provide insight into different aspects of data locality (or lack thereof). Unlike prior tools for data-centric analysis, our tool employs scalable measurement, analysis, and presentation methods that enable it to analyze the memory access behavior of scalable parallel programs with low runtime and space overhead. We demonstrate the utility of HPCToolkit's new data-centric analysis capabilities with case studies of five well-known benchmarks. In each benchmark, we identify performance bottlenecks caused by poor data locality and demonstrate non-trivial performance optimizations enabled by this guidance.

Chair/Author Details:

Shirley Moore (Chair) - University of Texas El Paso

Xu Liu - Rice University

John Mellor-Crummey - Rice University

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The full paper can be found in the ACM Digital Library