International Journal of Parallel Programming

, Volume 45, Issue 1, pp 4–16 | Cite as

An Optimal Page-Level Power Management Strategy in PCM–DRAM Hybrid Memory

  • Jinbao Zhang
  • Xiaofei Liao
  • Hai JinEmail author
  • Dong Liu
  • Li Lin
  • Kao Zhao


The new emergence of data-intensive applications raises a huge requirement on the capacity of memory. However, an obvious increase of memory makes the corresponding energy consumption unacceptable in practice. To address this question, any effort only to reduce the energy consumption of dynamic random access memory (DRAM) is ineffective. Recently, combining DRAM and phase change memory (PCM) to construct a hybrid main memory is recognized as a promising solution to distinctly reduce the energy consumption. In this paper, we propose a new page-level energy management strategy to optimize the energy consumption of the hybrid main memory. The proposed strategy records pages’ local and global access information by a new data structure, and then classifies pages by the access history, at last adaptively places PCM or DRAM pages according to the memory characteristics and remaps the migrated pages. Our experimental results show that our strategy can achieve 9.4 % of energy saving and 9.6 % of performance improvement at most compared with APG and PDRAM, which were proposed respectively by conferences RACS’12 and DAC’09.


Energy consumption Hybrid memory PCM 



This paper is supported by China National Natural Science Foundation under Grants Nos. 61322210, 61379135, Doctoral Fund of Ministry of Education of China under Grant No. 20130142110048, National High-tech Research and Development Program of China (863 Program) under Grant No. 2015AA015303.


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Jinbao Zhang
    • 1
  • Xiaofei Liao
    • 1
  • Hai Jin
    • 1
    Email author
  • Dong Liu
    • 1
  • Li Lin
    • 1
  • Kao Zhao
    • 1
  1. 1.Cluster and Grid Computing Lab, Services Computing Technology and System Lab, School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhanChina

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