Please use this identifier to cite or link to this item: https://doi.org/10.1109/TC.2015.2409847
Title: Rank-Aware Dynamic Migrations and Adaptive Demotions for DRAM Power Management
Authors: Lu, Yanchao
Wu, Donghong
He, Bingsheng 
Tang, Xueyan
Xu, Jianliang
Guo, Minyi
Keywords: Science & Technology
Technology
Computer Science, Hardware & Architecture
Engineering, Electrical & Electronic
Computer Science
Engineering
Demotion
energy consumption
main memory systems
in-memory processing
page migrations
MEMORY
Issue Date: 1-Jan-2016
Publisher: IEEE COMPUTER SOC
Citation: Lu, Yanchao, Wu, Donghong, He, Bingsheng, Tang, Xueyan, Xu, Jianliang, Guo, Minyi (2016-01-01). Rank-Aware Dynamic Migrations and Adaptive Demotions for DRAM Power Management. IEEE TRANSACTIONS ON COMPUTERS 65 (1) : 187-202. ScholarBank@NUS Repository. https://doi.org/10.1109/TC.2015.2409847
Abstract: Modern DRAM architectures allow a number of low-power states on individual memory ranks for advanced power management. Many previous studies have taken advantage of demotions on low-power states for energy saving. However, most of the demotion schemes are statically performed on a limited number of pre-selected low-power states, and are suboptimal for different workloads and memory architectures. Even worse, the idle periods are often too short for effective power state transitions, especially for memory intensive applications. Wrong decisions on power state transition incur significant energy and delay penalties. In this paper, we propose a novel memory system design named RAMZzz with rank-aware energy saving optimizations including dynamic page migrations and adaptive demotions. Specifically, we group the pages with similar access locality into the same rank with dynamic page migrations. Ranks have their hotness: hot ranks are kept busy for high utilization and cold ranks can have more lengthy idle periods for power state transitions. We further develop adaptive state demotions by considering all low-power states for each rank and a prediction model to estimate the power-down timeout among states. 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-delay 2 and energy consumption over other energy saving techniques.
Source Title: IEEE TRANSACTIONS ON COMPUTERS
URI: https://scholarbank.nus.edu.sg/handle/10635/215373
ISSN: 0018-9340
1557-9956
DOI: 10.1109/TC.2015.2409847
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