Rank-Aware Dynamic Migrations and Adaptive Demotions for DRAM Power Management
Yanchao Lu, Donghong Wu, Bingsheng He, Xueyan Tang, Jianliang Xu and, Minyi Guo

TL;DR
This paper introduces RAMZzz, a novel DRAM power management system that dynamically migrates pages and adaptively demotes ranks to optimize energy savings based on workload behavior.
Contribution
The paper presents a rank-aware energy saving approach with dynamic page migrations and adaptive demotions, improving upon static schemes for better energy efficiency.
Findings
Significant reduction in energy consumption compared to existing methods.
Improved energy-delay product performance across benchmark workloads.
Effective prediction model for power-down timeouts enhances power state transitions.
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…
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Taxonomy
TopicsParallel Computing and Optimization Techniques · Advanced Data Storage Technologies · Stochastic Gradient Optimization Techniques
