Dynamic Optimization through the use of Automatic Runtime Specialization

Profile-driven optimizations and dynamic optimization through specialization have taken optimizations to a new level. By using actual run-time data, optimizers can generate code that is specially tuned for the task at hand. However, most existing compilers that perform these optimizations require separate test runs to gather profile information, and/or user annotations in the code. In this thesis, I describe run-time optimizations that a dynamic compiler can perform automatically — without user annotations — by utilizing real-time performance data. I describe the implementation of the dynamic optimizations in the framework of a Java Virtual Machine and give performance results. Thesis Supervisor: Martin Rinard Title: Assistant Professor