Abstract

Dynamic program optimizations are critical for the efficiency of applications in managed programming languages and scripting languages. Recent studies have shown that exploitation of program inputs may enhance the effectiveness of dynamic optimizations significantly. However, current solutions for enabling the exploitation require either programmers’ annotations or intensive offline profiling, impairing the practical adoption of the techniques. This current work examines the basic feasibility of transparent integration of input-consciousness into dynamic program optimizations, particularly in managed execution environments. It uses transparent learning across production runs as the basic vehicle, and investigates the implications of cross-run learning on each main component of inputconscious

Citations