Combining Coarse-Grained Software Pipelining with DVS for Scheduling Real-Time Periodic Dependent Tasks on Multi-Core Embedded Systems

  • Authors:
  • Hui Liu;Zili Shao;Meng Wang;Junzhao Du;Chun Jason Xue;Zhiping Jia

  • Affiliations:
  • Software Engineering Institute, Xidian University, Xi'an, China 710071;Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong;Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong;Software Engineering Institute, Xidian University, Xi'an, China 710071;Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong;School of Computer Science and Technology, Shandong University, Jinan, China

  • Venue:
  • Journal of Signal Processing Systems
  • Year:
  • 2009

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Abstract

In this paper, we combine coarse-grained software pipelining with DVS (Dynamic Voltage/Frequency Scaling) for optimizing energy consumption of stream-based multimedia applications on multi-core embedded systems. By exploiting the potential of multi-core architecture and the characteristic of streaming applications, we propose a two-phase approach to solve the energy minimization problem for periodic dependent tasks on multi-core processors with discrete voltage levels. With our approach, in the first phase, we propose a coarse-grained task-level software pipelining algorithm called RDAG to transform the periodic dependent tasks into a set of independent tasks based on the retiming technique (Leiserson and Saxe, Algorithmica 6:5---35, 1991). In the second phase, we propose two DVS scheduling algorithms for energy minimization. For single-core processors, we propose a pseudo-polynomial algorithm based on dynamic programming that can achieve optimal solution. For multi-core processors, we propose a novel scheduling algorithm called SpringS which works like a spring and can effectively reduce energy consumption by iteratively adjusting task scheduling and voltage selection. We conduct experiments with a set of benchmarks from E3S (Dick 2008) and TGFF ( http://ziyang.ece.northwestern.edu/tgff/ ) based on the power model of the AMD Mobile Athlon4 DVS processor. The experimental results show that our technique can achieve 12.7% energy saving compared with the algorithms in Zhang et al. (2002) on average.