An Optimal Solution for the Heterogeneous Multi-processor Single-level Voltage Setup Problem

博士 === 國立清華大學 === 資訊工程學系 === 98 === A heterogeneous multi-processor (HeMP) system consists of several heterogeneous processors, each of which is specially designed to deliver the best energy-saving performance for a particular category of applications. A low power real-time scheduling algorithm is r...

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Bibliographic Details
Main Authors: Chu, Tsung-Hsien, 朱宗賢
Other Authors: King, Chung-Ta
Format: Others
Language:en_US
Published: 2010
Online Access:http://ndltd.ncl.edu.tw/handle/72251973646338623284
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Summary:博士 === 國立清華大學 === 資訊工程學系 === 98 === A heterogeneous multi-processor (HeMP) system consists of several heterogeneous processors, each of which is specially designed to deliver the best energy-saving performance for a particular category of applications. A low power real-time scheduling algorithm is required to schedule tasks on such a system to minimize its energy consumption and complete all tasks by their deadlines. Existing works assume that processor speeds are known as a priori and cannot deliver the optimal energy-saving performance. The problem of determining the optimal voltage for each processor in order to minimize the total energy consumption is called the voltage setup problem. To the best of our knowledge, this study is the first work to propose the optimal solution for the HeMP single-level voltage setup problem. We first formulate the problem as a non-linear generalized assignment problem that has been proved to be NP-hard. We next develop a pruning-based algorithm to obtain the optimal solution. A heuristic algorithm is also proposed to derive an approximate solution. In our simulations, we model more than several dozen of off-the-shelf embedded processors including ARM and TI DSP processors. The results show that the pruning-based algorithm reduces the time usually needed for an exhaustive search to derive the optimal solution by at least 98%. Also, our heuristic algorithm achieves a minimum energy consumption in comparison with existing research.