The Implementation of Static-Integrated Optimization Framework for Dynamic Binary Translation

In traditional dynamic binary translation (DBT) systems, poor profile information at runtime limits the manner of optimization. Combining dynamic binary translation with static analysis brings an opportunity to improve the runtime performance. Once the source image has been executed, the profile information and target code will be saved, which are available at next runs. Moreover, optimizations will be implemented during static analysis phase. This approach reduces the overhead of optimizing, profiling and translating on condition that the program runs repeatedly. In this paper, we accomplish to construct this framework (Sinof) and gain 5% performance benefits in average without completing static optimizations.