ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2015, Vol. 52 ›› Issue (6): 1303-1315.doi: 10.7544/issn1000-1239.2015.20150153

Special Issue: 2015面向应用领域需求的体系结构

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EOFDM: A Search Method for Energy-Efficient Optimization in Many-Core Architecture

Zhu Yatao1,2,5, Zhang Shuai3, Wang Da1, Ye Xiaochun1, Zhang Yang1,2, Hu Jiuchuan4, Zhang Zhimin1, Fan Dongrui1, Li Hongliang6   

  1. 1(State Key Laboratory of Computer Architecture(Institute of Computing Technology, Chinese Academy of Sciences), Beijing 100190);2(School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing 100049);3(National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing 100029);4(School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044);5(College of Information Science & Technology, Agricultural University of Hebei, Baoding, Hebei 071001);6(State Key Laboratory of Mathematical Engineering and Advanced Computing, Wuxi, Jiangsu 214125)
  • Online:2015-06-01

Abstract: Based on the optimization of energy consumption, “area-power” assignment is one of research issues in many-core processors. The distribution of area-power in space of core number and frequency level can be obtained form energy-performance model. Then the progressive search for optimal solutions of “core number and frequency level” configuration can be implemented in two dimensions. However, the existing methods of searching for energy-efficient optimization have slow convergence speed and great overhead of search in the space of core number and frequency level. Moreover, though searching for optimal core number and frequency level in the space composed by an analytical energy-performance model can reduce the overhead of real execution, the accuracy of optimal solution greatly depends on the misprediction of the model. Therefore, a search method based on FDM(EOFDM) is developed to reduce the dimensions of core number and frequency, and to involve the real energy and the performance of each feasible point to correct the model computation. The experimental results show that, compared with hill-climbing heuristic(HCH) in the execution times, the performance overhead and the energy overhead, our method makes an average reduction by 39.5%, 46.8%, 48.3%, and 48.8%, 51.6%, 50.9% in doubling the number of cores, and 45.5%, 49.8%, 54.4% in doubling the number of frequency levels. Our method is improved in convergence, search cost and scalability.

Key words: energy-efficient optimization, many-core, optimal solution, feasible direction method(FDM), hill-climbing heuristic(HCH)

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