An input-centric paradigm for program dynamic optimizations

Accurately predicting program behaviors (e.g., locality, dependency, method calling frequency) is fundamental for program optimizations and runtime adaptations. Despite decades of remarkable progress, prior studies have not systematically exploited program inputs, a deciding factor for program behaviors. Triggered by the strong and predictive correlations between program inputs and behaviors that recent studies have uncovered, this work proposes to include program inputs into the focus of program behavior analysis, cultivating a new paradigm named input-centric program behavior analysis. This new approach consists of three components, forming a three-layer pyramid. At the base is program input characterization, a component for resolving the complexity in program raw inputs and the extraction of important features. In the middle is input-behavior modeling, a component for recognizing and modeling the correlations between characterized input features and program behaviors. These two components constitute input-centric program behavior analysis, which (ideally) is able to predict the large-scope behaviors of a program's execution as soon as the execution starts. The top layer of the pyramid is input-centric adaptation, which capitalizes on the novel opportunities that the first two components create to facilitate proactive adaptation for program optimizations. By centering on program inputs, the new approach resolves a proactivity-adaptivity dilemma inherent in previous techniques. Its benefits are demonstrated through proactive dynamic optimizations and version selection, yielding significant performance improvement on a set of Java and C programs.