Characterizing Phase Behavior Through Time-Varying Microarchitecture Independent Characteristics Clustering
ResumoPrograms often exhibit repeating behaviors, which are known as program phases. The automatic discovery of such structured behavior has beneﬁted many applications. However, many existing phase signatures lack the ability to reason about what are the key factors of each phase. Also, programs exhibit phase behavior at many different granularities, and some exhibit hierarchical phase behavior. Many techniques focus on a single granularity, which can cause an out of sync classiﬁcation with the actual phase behavior. We solve these problems by adopting a recently proposed method of subsequence clustering of multivariate time series. Using this method, the phases started to have a much more interpretable signature (MRF). We graphically showed that the method partitions the execution into a temporally consistent way. We showed the effectiveness of MRF's signature by using a centrality measure to identify the most important characteristics within a program phase. Finally, we present a case study to show the relationship between the MRF signature and source code.
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