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 many different types of information for performing phase classification. The goal is to try to find the minimal amount of information to collect to accurately perform phase classification, and to do this without using architecture performance metrics. We compare using basic blocks, loop branches, procedures, opcode frequencies, register usage, register definitions, memory addresses, and working code and data set sizes. We also examine collecting this information in different data structures from working set bit vectors to frequency vectors. We compare these different structures in terms of their ability to create homogeneous phases. We then evaluate the performance of using the more promising of these structures to guide SimPoint.
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