Energy-efficient deadline scheduling for heterogeneous systems

  • Authors:

  • Yan Ma;Bin Gong;Ryo Sugihara;Rajesh Gupta

  • Affiliations:

  • School of Computer Science and Technology, Shandong University, Jinan Shandong 250101, China and Department of Computer Science and Engineering, University of California, San Diego, CA 92093, USA;School of Computer Science and Technology, Shandong University, Jinan Shandong 250101, China;Department of Computer Science and Engineering, University of California, San Diego, CA 92093, USA;Department of Computer Science and Engineering, University of California, San Diego, CA 92093, USA

  • Venue:
  • Journal of Parallel and Distributed Computing
  • Year:
  • 2012

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Abstract

Energy efficiency is a major concern in modern high performance computing (HPC) systems and a power-aware scheduling approach is a promising way to achieve that. While there are a number of studies in power-aware scheduling by means of dynamic power management (DPM) and/or dynamic voltage and frequency scaling (DVFS) techniques, most of them only consider scheduling at a steady state. However, HPC applications like scientific visualization often need deadline constraints to guarantee timely completion. In this paper we present power-aware scheduling algorithms with deadline constraints for heterogeneous systems. We formulate the problem by extending the traditional multiprocessor scheduling and design approximation algorithms with analysis on the worst-case performance. We also present a pricing scheme for tasks in the way that the price of a task varies as its energy usage as well as largely depending on the tightness of its deadline. Last we extend the proposed algorithm to the control dependence graph and the online case which is more realistic. Through the extensive experiments, we demonstrate that the proposed algorithm achieves near-optimal energy efficiency, on average 16.4% better for synthetic workload and 12.9% better for realistic workload than the EDD (Earliest Due Date)-based algorithm; The extended online algorithm also outperforms the EDF (Earliest Deadline First)-based algorithm with an average up to 26% of energy saving and 22% of deadline satisfaction. It is experimentally shown as well that the pricing scheme provides a flexible trade-off between deadline tightness and price.