Scheduling optimization in multicore multithreaded microprocessors through dynamic modeling

Abstract

Complexity in resource allocation grows dramatically as multiple cores and threads are implemented on Multicore Multi-threaded Microprocessors (MMMP). Such complexity is escalated with variations in workload behaviors. In an effort to support a dynamic, adaptive and scalable operating system (OS) scheduling policy for MMMP, architectural strategies are proposed to construct linear models to capture workload behaviors and then schedule threads according to their resource demands. This paper describes the design through three steps: in the first step we convert a static scheduling policy into a dynamic one, which evaluates the thread mapping pattern at runtime. In the second step we employ regression models to ensure that the scheduling policy is capable of responding to the changing behaviors of threads during execution. In the final step we limit the overhead of the proposed policy by adopting a heuristic approach, thus ensure the scalability with the exponential growth of core and thread counts. The experimental results validate our proposed model in terms of throughput, adaptability and scalability. Compared with the baseline static approach, our phase-triggered scheduling policy could achieve up to 29% speedup. We also provide detailed tradeoff study between performance and overhead that system architects can reference to when target systems and specific overheads are presented.