Abstract :
Accurate performance diagnosis of parallel and distributed programs is a difficult and time-consuming task. We describe a new technique that uses historical performance data, gathered in previous executions of an application, to increase the effectiveness of automated performance diagnosis. We incorporate several different types of historical knowledge about the application’s performance into an existing profiling tool, the Paradyn Parallel Performance Tool. We gather performance and structural data from previous executions of the same program, extract knowledge useful for diagnosis from this collection of data in the form of search directives, then input the directives to an enhanced version of Paradyn, which conducts a directed online diagnosis. Compared to existing approaches, incorporating historical data shortens the time required to identify bottlenecks, decreases the amount of unhelpful instrumentation, and improves the usefulness of the information obtained from a diagnostic session.
