Energy efficient multiprocessor task scheduling under input-dependent variation

  • Authors:
  • Jason Cong;Karthik Gururaj

  • Affiliations:
  • University of California, Los Angeles, CA;University of California, Los Angeles, CA

  • Venue:
  • Proceedings of the Conference on Design, Automation and Test in Europe
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

In this paper, we propose a novel, energy aware scheduling algorithm for applications running on DVS-enabled multiprocessor systems, which exploits variation in execution times of individual tasks. In particular, our algorithm takes into account latency and resource constraints, precedence constraints among tasks and input-dependent variation in execution times of tasks to produce a scheduling solution and voltage assignment such that the average energy consumption is minimized. Our algorithm is based on a mathematical programming formulation of the scheduling and voltage assignment problem and runs in polynomial time. Experiments with randomly generated task graphs show that up to 30% savings in energy can be obtained by using our algorithm over existing techniques. We perform experiments on two real-world applications -- MPEG-4 decoder and MJPEG encoder. Simulations show that the scheduling solution generated by our algorithm can provide up to 25% reduction in energy consumption over greedy dynamic slack reclamation algorithms.