Emprical study on Reducing Energy of Parallel Programs using Slack Reclamation by DVFS in a Power-scalable High Performance Cluster

Abstract

It has become important to improve the energy efficiency of high performance PC clusters. In PC clusters, high-performance microprocessors have a dynamic voltage and frequency scaling (DVFS) mechanism, which allows the voltage and frequency to be set for reduction in energy consumption. In this paper, we proposed a new algorithm that reduces energy consumption in a parallel program executed on a power-scalable cluster using DVFS. Whenever the computational load is not balanced, parallel programs encounter slack time, that is, they must wait for synchronization of the tasks. Our algorithm reclaims slack time by changing the voltage and frequency, which allows a reduction in energy consumption without impacting on the performance of the program. Our algorithm can be applied to parallel programs represented by a directed acyclic task graph (DAG). It selects an appropriate set of voltages and frequencies (called the gear) that allow the tasks to execute at the lowest frequency that does not increase the overall execution time, but at the same time allows the tasks to be executed as uniformly as possible in frequency. We built two different types of power-scalable clusters using AMD Turion and Transmeta Crusoe. For the empirical study on energy reduction in PC clusters, we designed a toolkit called PowerWatch that includes power monitoring tools and the DVFS control library. This toolkit precisely measures the power consumption of the entire cluster in real time. The experimental results using benchmark problems show that our algorithm reduces energy consumption by 25% with only a 1 % loss in performance