Exploiting statistical correlations for proactive prediction of program behaviors

Abstract

This paper presents a finding and a technique on program behavior prediction. The finding is that surprisingly strong statistical correlations exist among the behaviors of different program components (e.g., loops) and among different types of program level behaviors (e.g., loop trip-counts versus data values). Furthermore, the correlations can be beneficially exploited: They help resolve the proactivity-adaptivity dilemma faced by existing program behavior predictions, making it possible to gain the strengths of both approaches--the large scope and earliness of offline-profiling--based predictions, and the cross-input adaptivity of runtime sampling-based predictions.