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Low-cost estimation of sub-system power

机译:低成本估算子系统功率

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Real-time, fine-grained power consumption information enables energy optimization and adaptation for operating systems and applications. Due to the high cost associated with dedicated power sensors, however, most computers do not have the ability to measure disaggregated power consumption at a component or subsystem level. We present DiPART (Disaggregated Power Analysis in Real Time), a tool to estimate subsystem power consumption based on performance (event) counters and a single, system-wide power sensor. With only one power sensor for overall system power consumption, DiPART is able to self-adapt to variations in subsystem power consumption present across nominally identical hardware. We validate the approach using a cluster of Intel Atom-based nodes that has been instrumented for subsystem (CPU, RAM and disk) power measurements. DiPART was tested across nodes in the cluster using varied benchmarks, resulting in a 40% reduction in estimation error when compared to a static model.
机译:实时,细粒度的功耗信息可实现能源优化和适用于操作系统和应用程序。但是,由于与专用功率传感器相关的高昂成本,大多数计算机不具备在组件或子系统级别上测量分类功耗的能力。我们介绍了DiPART(实时分布式功率分析),一种基于性能(事件)计数器和单个系统级功率传感器估算子系统功耗的工具。仅用一个功率传感器来测量整个系统的功耗,DiPART就能适应名义上相同的硬件中子系统功耗的变化。我们使用基于Intel Atom的节点集群对这种方法进行了验证,该集群已进行子系统(CPU,RAM和磁盘)功率测量。 DiPART在群集中的各个节点上使用各种基准进行了测试,与静态模型相比,可将估计误差降低40%。

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