Offline Phase Analysis and Optimization for Multi-configuration Processors

Abstract

Energy consumption has become a major issue for modern microprocessors. In previous work, several techniques were presented to reduce the overall energy consumption by dynamically adapting various hardware structures. Most approaches however lack the ability to deal efficiently with the huge amount of possible hardware configurations in case of multiple adaptive structures. In this paper, we present a framework that is able to deal with this huge configuration space problem. We first identify phases through profiling and determine the optimal hardware configuration per phase using an efficient offline search algorithm. During program execution, we inspect the phase behavior and adapt the hardware on a per-phase basis. This paper also proposes a new phase classification scheme as well as a phase correspondence metric to quantify the phase similarity between different runs of a program. Using SPEC2000 benchmarks, we show that our adaptive processing framework achieves an energy reduction of 40% on average with an average performance degradation of only 2%.