Understanding program behavior is at the foundation of computer architecture and program optimization. Many programs have wildly different behavior on even the very largest of scales (over the complete execution of the program). Even so, programs tend to have repetitive behavior, where different parts of a program's execution behave in a similar manner. These similar intervals of execution can be grouped into phases, where the intervals in a phase have homogeneous behavior and similar resource requirements. This phase behavior can be exploited by tailoring architecture or compiler optimizations to a given phase, rather than at average or aggregate behavior as is typically done. In this paper, we compare using different levels of information for performing phase classification from the memory address stream to the code being executed, and their combination. We also compare using different representations of the information for performing phase classification from working set size, to working set bit vectors, and frequency vectors. We combine this with a comparison of different algorithms for creating a phase classification. We consider the use of vector difference and the k-means algorithms from machine learning to classify phases. For the code analysis, we examine the tradeoffs of tracking phase behavior with different code constructs from using only procedures, to only loop branches, to using basic blocks, and their combination. Finally, we examine using data stream profiles to improve the homogeneity of code-based phase classification.
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