Algorithms for CPU and DRAM DVFS under inefficiency constraints
Algorithms for CPU and DRAM DVFS under inefficiency constraints
复制标题
低效率约束下的CPU和DRAM DVFS算法
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Geoffrey Challen
中科院分区：
文献类型：
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作者：
R. Begum;Mark Hempstead;Guru Prasad Srinivasa;Geoffrey Challen
内核和 DRAM 的动态电压和频率调节 (DVFS) 提供了权衡性能以节省能源的机会。以前使用 DVFS 进行核心和 DRAM 电源管理的方法使用性能（特别是可接受的性能损失）作为约束。我们提出了能源管理算法，可在指定的能源预算下协调核心和 DRAM 频率缩放。正如我们将展示的，在性能约束下工作的方法并不直接适用于在能源约束下运行的系统，因为很难实时计算正确的性能界限以保持在能源预算之下。为不同的应用程序设置任意的能源预算可能会损害应用程序性能。我们使用之前介绍的低效率概念（高于可用于提高性能的最低所需能量的额外能量），为我们的系统提供动态能量约束。我们引入了新的电源管理算法，可以搜索功率和性能空间，以找到在此约束下的最佳性能点。我们使用 CPU DVFS 和 DRAM 频率缩放来展示我们算法的功效。我们证明，与最先进的性能受限系统相比，我们的算法的调整成本降低了 24%，节省了高达 5% 的能源，并且性能损失很小。
Dynamic voltage and frequency scaling (DVFS) of both the core and DRAM provides opportunities to trade-off performance in order to save energy. Previous approaches to core and DRAM power management using DVFS used performance, specifically acceptable performance loss, as a constraint. We present energy management algorithms that coordinate core and DRAM frequency scaling under a specified energy budget. Approaches that work under performance constraints, as we will show, are not directly applicable to systems operating under energy constraints, as it is difficult to calculate the correct performance bounds in real-time to stay under an energy budget. Setting arbitrary energy budgets for a diverse set of applications can be harmful to application performance. We use the previously introduced concept of Inefficiency - the additional amount of energy above the minimum required energy that can be used to improve performance - to provide a dynamic energy constraint to our system. We introduce new power management algorithms that search the power and performance space to find the best performing point under this constraint. We demonstrate the efficacy of our algorithms using CPU DVFS and DRAM frequency scaling. We show that our algorithms have 24% lower tuning cost and save up to 5% energy with a little performance loss compared to a state-of-the-art performance constrained system.