Hybrid global/local search strategies for dynamic voltage scaling in embedded multiprocessors

In this paper, we explore a hybrid global/local search optimization framework for dynamic voltage scaling in embedded multiprocessor systems. The problem is to find, for a multiprocessor system in which the processors are capable of dynamically varying their core voltages, the optimum voltage levels for all the tasks in order to minimize the average power consumption under a given performance constraint. An effective local search approach for static voltage scaling based on the concept of a period graph has been demonstrated previously. To make use of it in an optimization problem, the period graph must be integrated into a global search algorithm. Simulated heating, a general optimization framework developed previously, is an efficient method for precisely this purpose of integrating local search into global search algorithms. However, little is known about the management of computational (compile-time) resources between global search and local search in hybrid algorithms, such as those coordinated by simulated heating. In this paper, we explore various hybrid search management strategies for power optimization under the framework of simulated heating. We demonstrate that careful search management leads to significant power consumption improvement over add-hoc global search/local search integration, and explore alternative approaches to performing hybrid search management for dynamic voltage scaling