Proceedings of the ACM International Conference on Computing Frontiers, Ischia, Italy, May 2011.
Characterizing the dynamic behavior of a program's execution is essential for optimizing the program on a given system. Once the program's repetitive execution phases (and their boundaries) have been correctly identified, various phase-aware optimization techniques can be applied. Multithreaded workloads exhibit dynamic behavior that is further affected by the sharing of data and platform resources. As computer systems and workloads become denser and more parallel, this effect is expected to intensify the dynamicity of the executed workload. In this work, we introduce a new relaxed concept for a parallel program phase, called epoch. Epochs are defined as time intervals between global synchronization points that programmers insert into their program codes for correct parallel execution. We characterize the behavior of multithreaded workloads across and within epochs and show that epochs have consistent and repetitive behaviors while their boundaries naturally indicate a shift in program behavior. We show that epoch changes can be easily captured at run time without complex monitoring and decision mechanisms and we employ simple run-time techniques to enable epoch based adaptation. To highlight the efficacy of our approach, we present a case study of an epoch-based adaptive chip multiprocessor (CMP) architecture. We conclude that our approach provides an attractive new framework for lightweight phase-based resource management for future CMPs.