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Publication Details for Inproceedings "Energy Minimization for Periodic Real-Time Tasks on Heterogeneous Processing Units"

 

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Authors: Jian-Jia Chen, Andreas Schranzhofer, Lothar Thiele
Group: Computer Engineering
Type: Inproceedings
Title: Energy Minimization for Periodic Real-Time Tasks on Heterogeneous Processing Units
Year: 2009
Month: May
Pub-Key: IPDPS 2009
Book Titel: 2009 IEEE International Symposium on Parallel & Distributed Processing
Pages: 1-12
Keywords: DSE
Publisher: IEEE
Abstract: Adopting multiple processing units to enhance the computing capability or reduce the power consumption has been widely accepted for designing modern computing systems. Such configurations impose challenges on energy efficiency in hardware and software implementations. This work targets power-aware and energy-efficient task partitioning and processing unit allocation for periodic real-time tasks on a platform with a library of applicable processing unit types. Each processing unit type has its own power consumption characteristics for maintaining its activeness and executing jobs. We show that there does not exist any polynomial-time approximation algorithm with a constant approximation factor unless P= NP. This paper proposes polynomial-time algorithms for energy-aware task partitioning and processing unit allocation. The proposed algorithms first decide how to assign tasks onto processing unit types to minimize the energy consumption, and then allocate processing units to fit the demands. The proposed algorithms for systems without limitation on the allocated processing units are shown with an (m+1)-approximation factor, where m is the number of the available processing unit types. For systems with limitation on the number of the allocated processing units, the proposed algorithm is shown with bounded resource augmentation on the limited number of allocated units. Experimental results show that the proposed algorithms are effective for the minimization of the overall energy consumption.
Location: International Parallel and Distributed Processing Symposium (IPDPS), Rome Italy
Resources: [BibTeX] [Paper as PDF]

 

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