Real-time statistical clustering for event trace reduction

Event tracing provides the detailed data needed to under-stand the dynamics of interactions among application resource demands and system responses. However, cap-turing the large volume of dynamic performance data inherent in detailed tracing can perturb program execution and stress secondary storage systems. Moreover, it can overwhelm a user or performance analyst with potentially irrelevant data. Using the Pablo performance environ-ment’s support for real-time data analysis, we show that dynamic statistical data clustering can dramatically reduce the volume of captured performance data by identifying and recording event traces only from representative proc-essors. In turn, this makes possible low overhead, interac-tive visualization, and performance tuning