Complete System Power Estimation: A Trickle-Down Approach Based on Performance Events

This paper proposes the use of microprocessor performance counters for online measurement of complete system power consumption. While past studies have demonstrated the use of performance counters for microprocessor power, to the best of our knowledge, we are the first to create power models for the entire system based on processor performance events. Our approach takes advantage of the "trickle-down" effect of performance events in a microprocessor. We show how well known performance-related events within a microprocessor such as cache misses and DMA transactions are highly correlated to power consumption outside of the microprocessor. Using measurement of an actual system running scientific and commercial workloads we develop and validate power models for five subsystems: memory, chipset, I/O, disk and microprocessor. These models are shown to have an average error of less than 9% per subsystem across the considered workloads. Through the use of these models and existing on-chip performance event counters, it is possible to estimate system power consumption without the need for additional power sensing hardware

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