Automated Fingerprinting of Performance Pathologies Using Performance Monitoring Units ( PMUs )

Modern architectures provide access to many hardware performance events, which are capable of providing insight into architectural performance bottlenecks throughout the core and memory hierarchy. These events can provide programmers with unique and powerful insights into the causes of performance problems in their programs, but interpreting these events has been a significant challenge. We describe a technique that uses data mining to automatically fingerprint a program’s performance problems, permitting programmers to reap the architectural insights made possible by the events while shielding them from the onerous task of interpreting raw events. We use a decision tree algorithm on a set of micro-benchmarks to construct a model of performance problems. This extracted model is able to divide a profiled application into program phases, and label the phases with the patterns of hardware bottlenecks. Our framework provides programmers with a detailed map of what to optimize in their code, sparing them the need to interpret raw events.