An Adaptive Resource Partitioning Algorithm for SMT processors

Abstract

Simultaneous Multithreading (SMT) increases processor throughput by allowing the parallel execution of several threads. However, fully sharing processor resources may cause resource monopolization by a single thread or other misallocations, resulting in overall performance degradation. Static resource partitioning techniques have been suggested, but are not as effective as dynamically controlling the resource usage of each thread since program behavior does change during its execution. In this paper, we propose an Adaptive Resource Partitioning Algorithm (ARPA) that dynamically assigns resources to threads according to thread behavior changes. ARPA analyzes the resource usage efficiency of each thread in a time period and assigns more resources to threads which can use them in a more efficient way. The purpose of ARPA is to improve the efficiency of resource utilization, thereby improving overall instruction throughput. Our simulation results on a set of 42 multiprogramming workloads show that ARPA outperforms the traditional fetch policy ICOUNT by 55.8% with regard to overall instruction throughput and achieves a 33.8% improvement over Static Partitioning. It also outperforms the current best dynamic resource allocation technique, Hill-climbing, by 5.7%. Considering fairness accorded to each thread, ARPA attains 43.6%, 18.5% and 9.2% improvements over ICOUNT, Static Partitioning and Hill-climbing, respectively, using a common fairness metric.