Dynamic Optimization on Multi-core Platform

Applications involve dynamic linked library or legacy code which is difficult to optimize for static compiler, however, can be applied with optimization techniques by run-time system who focuses on binary code instead of source code. In this paper, we present a run-time system which observes the behaviors of the applications and performs optimizations base on the information it collects, and it works on the multi-core platform to boost performance. First, we establish a framework of traditional dynamic optimization system, base on which we propose the ways to reduce the overhead of the system, including different ways of trace construction and basic block linking. Second, we take advantage of hardware resources that multi-core platform provides and exploit parallelism in our system. Finally, we evaluate the performance of our system and have two major results. First, basic block linking can greatly improve the performance of the system, because without it, the system spends much of its time on interpreting and context switching. Second, the benchmarks that running on the system have a speedup of 1.05 on average, depending on the program's behavior.