RAMZzz : rank-aware DRAM power management with dynamic migrations and demotions
Main memory is a significant energy consumer which may contribute to over 40% of the total system power, and will become more significant for server machines with more main memory. In this paper, we propose a novel memory system design named RAMZzz with rank-aware energy saving optimizations. Specif...
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sg-ntu-dr.10356-1061752020-05-28T07:18:36Z RAMZzz : rank-aware DRAM power management with dynamic migrations and demotions Wu, Donghong He, Bingsheng Tang, Xueyan Xu, Jianliang Guo, Minyi School of Computer Engineering International Conference for High Performance Computing, Networking, Storage and Analysis (2012 : Salt Lake City, US) DRNTU::Engineering::Computer science and engineering Main memory is a significant energy consumer which may contribute to over 40% of the total system power, and will become more significant for server machines with more main memory. In this paper, we propose a novel memory system design named RAMZzz with rank-aware energy saving optimizations. Specifically, we rely on a memory controller to monitor the memory access locality, and group the pages with similar access locality into the same rank. We further develop dynamic page migrations to adapt to data access patterns, and a prediction model to estimate the demotion time for accurate control on power state transitions. We experimentally compare our algorithm with other energy saving policies with cycle-accurate simulation. Experiments with benchmark workloads show that RAMZzz achieves significant improvement on energy-delay2 and energy consumption over other power saving techniques. 2013-10-18T07:36:44Z 2019-12-06T22:05:49Z 2013-10-18T07:36:44Z 2019-12-06T22:05:49Z 2012 2012 Conference Paper Wu, D., He, B., Tang, X., Xu, J., & Guo, M. (2012). RAMZzz : rank-aware DRAM power management with dynamic migrations and demotions. 2012 International Conference for High Performance Computing, Networking, Storage and Analysis(SC), 1-11 . https://hdl.handle.net/10356/106175 http://hdl.handle.net/10220/16631 10.1109/SC.2012.99 en © 2012 IEEE |
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DRNTU::Engineering::Computer science and engineering Wu, Donghong He, Bingsheng Tang, Xueyan Xu, Jianliang Guo, Minyi RAMZzz : rank-aware DRAM power management with dynamic migrations and demotions |
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Main memory is a significant energy consumer which may contribute to over 40% of the total system power, and will become more significant for server machines with more main memory. In this paper, we propose a novel memory system design named RAMZzz with rank-aware energy saving optimizations. Specifically, we rely on a memory controller to monitor the memory access locality, and group the pages with similar access locality into the same rank. We further develop dynamic page migrations to adapt to data access patterns, and a prediction model to estimate the demotion time for accurate control on power state transitions. We experimentally compare our algorithm with other energy saving policies with cycle-accurate simulation. Experiments with benchmark workloads show that RAMZzz achieves significant improvement on energy-delay2 and energy consumption over other power saving techniques. |
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School of Computer Engineering |
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School of Computer Engineering Wu, Donghong He, Bingsheng Tang, Xueyan Xu, Jianliang Guo, Minyi |
format |
Conference or Workshop Item |
author |
Wu, Donghong He, Bingsheng Tang, Xueyan Xu, Jianliang Guo, Minyi |
author_sort |
Wu, Donghong |
title |
RAMZzz : rank-aware DRAM power management with dynamic migrations and demotions |
title_short |
RAMZzz : rank-aware DRAM power management with dynamic migrations and demotions |
title_full |
RAMZzz : rank-aware DRAM power management with dynamic migrations and demotions |
title_fullStr |
RAMZzz : rank-aware DRAM power management with dynamic migrations and demotions |
title_full_unstemmed |
RAMZzz : rank-aware DRAM power management with dynamic migrations and demotions |
title_sort |
ramzzz : rank-aware dram power management with dynamic migrations and demotions |
publishDate |
2013 |
url |
https://hdl.handle.net/10356/106175 http://hdl.handle.net/10220/16631 |
_version_ |
1681057959005650944 |