用于DRAM电源管理的等级感知动态迁移和自适应降级
Rank-Aware Dynamic Migrations and Adaptive Demotions for DRAM Power Management
摘要：
现代DRAM架构允许在各个存储器列上的多个低功率状态，以用于高级功率管理。许多先前的研究已经利用了低功率状态的降级来节省能量。然而，大多数降级方案是在有限数量的预先选择的低功率状态上静态地执行的，并且对于不同的工作负载和存储器架构是次优的。更糟糕的是，空闲周期对于有效的功率状态转换来说通常太短，特别是对于存储器密集型应用。功率状态转换的错误决策会导致显著的能量和延迟损失。在本文中，我们提出了一种新的内存系统设计命名为RAMZzz与排名意识的节能优化，包括动态页面迁移和自适应降级。具体来说，我们组的页面具有类似的访问本地到相同的排名与动态页面迁移。等级有其热性：热等级保持忙碌以获得高利用率，而冷等级可以具有更长的空闲时间以进行功率状态转换。我们进一步发展自适应状态降级，考虑所有低功耗状态的每个等级和预测模型，以估计状态之间的断电超时。我们实验比较我们的算法与其他节能政策与周期准确的模拟。基准负载的实验表明，RAMZzz实现了显着改善能源延迟2和能源消耗比其他节能技术。
英文摘要：
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-delay2 and energy consumption over other energy saving techniques.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
