TL;DRAbstract
Performance modeling, the science of understanding and predicting application performance, is important but challenging. High Performance Computing (HPC) with large- scale applications and aggressive technologies, such as dynamic computational grids, hybrid computing platforms, and innovative storage systems, further complicates the task. This dissertation proposed and proved the hypothesis that a small number of performance primitives can be extracted from HPC applications and leveraged for fast application performance modeling and prediction even on large-scale dynamic systems. PMap : a set of methods and tools to extract, measure, and analyze performance primitives in HPC applications are proposed, implemented, and verified under these challenging environments. Two production computational grids, Teragrid and Geon, were monitored with periodically running benchmarks for about half a year. Their performance fluctuated in the 50% range. However, simple benchmarks that serve as perform
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Performance modeling, the science of understanding and predicting application performance, is important but challenging. High Performance Computing (HPC) with large- scale applications and aggressive technologies, such as dynamic computational grids, hybrid computing platforms, and innovative storage systems, further complicates the task. This dissertation proposed and proved the hypothesis that a small number of performance primitives can be extracted from HPC applications and leveraged for fast application performance modeling and prediction even on large-scale dynamic systems. PMap : a set of methods and tools to extract, measure, and analyze performance primitives in HPC applications are proposed, implemented, and verified under these challenging environments. Two production computational grids, Teragrid and Geon, were monitored with periodically running benchmarks for about half a year. Their performance fluctuated in the 50% range. However, simple benchmarks that serve as perform
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