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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. [ABSTRACT FROM AUTHOR]

Main memory (or DRAM) is one of the most significant components to the computer system’s performance and energy consumption. Dynamic frequency scaling (DFS) and DRAM low-power states (Demotion ) are two main-stream techniques for DRAM power management. DFS reduces the operation frequency of memory channels and DRAM devices when the memory bandwidth is under-utilized, whereas demotion transits individual memory ranks to low-power states during long idle periods. Despite that there have been fruitful research work for DFS and demotion separately, little attention has been paid to the synergy between these two techniques. To bridge this gap, this paper conducts a comprehensive study on the synergy between DFS and demotion. In particular, we leverage queuing theory to develop analytical models for the energy consumption and performance of DRAM systems with DFS and demotion. These models provide valuable insights into the synergy between DFS and demotion. We further attempt to minimize the energy consumption by considering both DFS and demotion while keeping a pre-defined performance penalty budget. To reduce the optimization complexity, we develop simple yet effective heuristics to search near-optimum DFS-demotion configurations. We experimentally compare our design with other state-of-the-art DRAM energy saving policies using detailed simulations of a large set of workloads. Experimental results show the accuracy of our analytical models and the effectiveness of our optimizations. [ABSTRACT FROM AUTHOR]