Hyperheuristics for explicit resource partitioning in simultaneous multithreaded processors

Abstract

In simultaneous multithreaded (SMT) processors, various data path resources are concurrently shared bymany threads. A few heuristic approaches that explicitly distribute those resources among threads with the goal ofimproved overall performance have already been proposed. A selection hyperheuristic is a high-level search methodologythat mixes a predetermined set of heuristics in an iterative framework to utilize their strengths for solving a givenproblem instance. In this study, we propose a set of selection hyperheuristics for selecting and executing the heuristicwith the best performance at a given stage. To the best of our knowledge, this is one of the first studies implementinga hyperheuristic algorithm on hardware. The results of our experimental study show that hyperheuristics are indeedcapable of improving the performance of the studied workloads. Our best performing hyperheuristic achieves betterthroughput than both baseline heuristics in 5 out of 12 workloads and gives about 15% peak performance gain. Theaverage performance gains over the well-known hill-climbing and adaptive resource partitioning heuristics are about 5%and 2%, respectively.