Most programs are repetitive, where similar behavior can be seen at different execution times. Algorithms have been proposed that automatically group similar portions of a program's execution into phases, where samples of execution in the same phase have homogeneous behavior and similar resource requirements. In this paper, we present an automated profiling approach to identify code locations whose executions correlate with phase changes. These ``software phase markers'' can be used to easily detect phase changes across different inputs to a program without hardware support. Our approach builds a combined hierarchical procedure call and loop graph to represent a program's execution, where each edge also tracks the average hierarchical execution variability on paths from that edge. We search the annotated call-loop graph for instructions in the binary that accurately identify phase changes across different inputs. We show that our phase markers can be used to accurately partition execution into units of repeating homogeneous behavior by counting cycles and data cache hits. We also use the markers to guide dynamic data cache reconfiguration, and show cache size reductions comparable to more complex cache reconfiguration techniques.
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