Dominant Variance Characterization
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There are a whole range of program analysis techniques that characterize different aspects of an application’s performance: hot-spots, distinct phases of behavior, code segments that could potentially run in parallel, etc. For a growing class of applications, there is a need to add another analysis technique to the repertoire that can characterize the locations and underlying causes of execution time variance in repetitive parts of the application. In this paper we introduce the notion of dominant variance analysis of an application. We illustrate the unique performance optimization benefits of performing such an analysis. We motivate that traditional program analysis and profiling techniques are not sufficient to analyze the variant execution time behavior of the application. We introduce a new program representation called Variance Characterization Graph that is used both as the intermediate representation to enable the dominant variance analysis and as the final representation that provides concise and actionable information to programmers. We identify the unique challenges associated with characterizing the dominant behavior of an application and develop a methodology based on statistical pattern matching to efficiently recognize dominant patterns of behavior.
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