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Trace Spectral Analysis toward Dynamic Levels of Detail

机译:动态细节水平的痕量光谱分析

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The emergence of Petascale systems has raised new challenges to performance analysis tools. Understanding every single detail of an execution is important to bridge the gap between the theoretical peak and the actual performance achieved. Tracing tools are the best option when it comes to providing detailed information about the application behavior, but not without liabilities. The amount of information that a single execution can generate grows so fast that it easily becomes unmanageable. An effective analysis in such scenarios necessitates the intelligent selection of information. In this paper we present an on-line performance tool based on spectral analysis of signals that automatically identifies the different computing phases of the application as it runs, selects a few representative periods and decides the granularity of the information gathered for these regions. As a result, the execution is completely characterized at different levels of detail, reducing the amount of data collected while maximizing the amount of useful information presented for the analysis.
机译:Petascale系统的出现给性能分析工具提出了新的挑战。了解执行的每个细节对于弥合理论峰值与实际性能之间的差距非常重要。跟踪工具是提供有关应用程序行为的详细信息的最佳选择,但并非没有责任。一次执行可以生成的信息量增长如此之快,以至于很容易变得难以管理。在这种情况下,要进行有效的分析,就必须明智地选择信息。在本文中,我们提供了一种基于信号频谱分析的在线性能工具,该工具可以在运行时自动识别应用程序的不同计算阶段,选择几个代表周期并确定针对这些区域收集的信息的粒度。结果,可以在不同的细节级别上对执行过程进行完全表征,从而减少了收集的数据量,同时最大化了为分析提供的有用信息量。

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