Adaptive online context-sensitive inlining

As current trends in software development move toward more complex object-oriented programming, inlining has become a vital optimization that provides substantial per-formance improvements to C++ and Java programs. Yet, the aggressiveness of the inlining algorithm must be care-fully monitored to effectively balance performance and code size. The state-of-the-art is to use profile information (as-sociated with call edges) to guide inlining decisions. In the presence of virtual method calls, profile information for one call edge may not be sufficient for making effectual inlining decisions. Therefore, we explore the use of profiling data with additional levels of context sensitivity. In addition to exploring fixed levels of context sensitivity, we explore sev-eral adaptive schemes that attempt to find the ideal degree of context sensitivity for each call site. Our techniques are evaluated on the basis of runtime performance, code size and dynamic compilation time. On average, we found that with minimal impact on performance (+/-1%) context sensitivity can enable 10 % reductions in compiled code space and com-pile time. Performance on individual programs varied from-4.2 % to 5.3 % while reductions in compile time and code space of up to 33.0 % and 56.7 % respectively were obtained. 1