Algorithmic Support for Power Management

电源管理的算法支持

• 批准号: 0830558
• 负责人: Kirk Pruhs
• 金额: $ 29.99万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0830558&HistoricalAwards=false
• 关键词: Algorithmic; Support; Power; Management

Power is now widely recognized as a first class architectural design constraint at all levels of computing, from the level of processor chips to the level of data centers. There are at least three distinct common goals for power management: energy conservation, temperature management, and limiting maximum power. The most commonly used power management technique is speed scaling, which involves changing the speed of the processor. Essentially all currently produced general purpose processors may be run at multiple speeds, and all leading processor manufacturers produce associated software that manages power by scaling processor speed. However, speed scaling as a power management technique, has to date mostly been applied in a reactive fashion in that it is usually only invoked in response to abnormal conditions, and its application is largely decoupled from the scheduling policies in that they are working independently of each other. It is widely accepted that integrated proactive power management and scheduling policies will result in better power management than simple reactive strategies.The proposed research investigates algorithmic problems related to integrated power management and and scheduling. The resulting optimization problems have dual objectives, since one seeks to optimize both some quality-of-service measure of the schedule, and some power related criteria. In general these objectives are conflicting in that the more power that one uses, the better quality of service that can be provided. The algorithmic solutions to these problem then involve increasing power when the improvement in the scheduling objective justifies the increased cost in the power management objective. The goals of the research are threefold. The first goal is to develop algorithms that could form the basis for future power management software. The second goal is to build a toolkit of widely applicable algorithmic methods for power management problems. The third goal is help increase the computing community's ability to abstractly reason about power, energy and temperature.

现在，功耗被广泛认为是所有计算级别（从处理器芯片级别到数据中心级别）的一流架构设计约束。 电源管理至少有三个不同的共同目标：节能、温度管理和限制最大功率。 最常用的电源管理技术是速度缩放，这涉及改变处理器的速度。 本质上，当前生产的所有通用处理器都可以以多种速度运行，并且所有领先的处理器制造商都生产通过扩展处理器速度来管理功率的相关软件。 然而，速度缩放作为一种电源管理技术，迄今为止大多以反应式方式应用，因为它通常仅在响应异常情况时被调用，并且其应用在很大程度上与调度策略脱钩，因为它们独立于调度策略工作。彼此。 人们普遍认为，集成的主动电源管理和调度策略将比简单的反应策略带来更好的电源管理。本文研究了与集成电源管理和调度相关的算法问题。 由此产生的优化问题具有双重目标，因为人们寻求优化调度的某些服务质量测量以及某些与功率相关的标准。 一般来说，这些目标是相互冲突的，因为使用的功率越多，可以提供的服务质量就越好。 当调度目标的改进证明功率管理目标中增加的成本合理时，这些问题的算法解决方案涉及增加功率。 该研究的目标有三个。 第一个目标是开发可以构成未来电源管理软件基础的算法。 第二个目标是构建一个针对电源管理问题的广泛适用的算法方法工具包。 第三个目标是帮助提高计算社区抽象推理功率、能量和温度的能力。