Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/28460
Title: Combining coarse-grained software pipelining with dvs for scheduling real-time periodic dependent tasks on multi-core embedded systems
Authors: Liu, H
Shao, Z 
Wang, M
Du, J
Xue, CJ
Jia, Z
Keywords: Dynamic voltage scaling (DVS)
Multi-core
Multimedia
Periodic dependent tasks
Real-time
Retiming
Scheduling
Software pipelining
Issue Date: 2009
Publisher: Springer
Source: Journal of signal processing systems, 2009, v. 57, no. 2, p. 249-262 How to cite?
Journal: Journal of signal processing systems 
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.
URI: http://hdl.handle.net/10397/28460
ISSN: 1939-8018
EISSN: 1939-8115
DOI: 10.1007/s11265-008-0315-2
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