BarrierWatch: characterizing multithreaded workloads across and within program-defined epochs

Abstract

Characterizing the dynamic behavior of a program 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 optimizations 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 will 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.