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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Simulation of the absorber layer thickness effect on the performance of CuInSe <sub/>2</sub> solar cells
摘要： Quantitatively estimating the relationship between the workload and the corresponding power consumption of a multicore processor is an essential step towards achieving energy proportional computing. Most existing and proposed approaches use Performance Monitoring Counters (Hardware Monitoring Counters) for this task. In this paper we propose a complementary approach that employs the statistics of CPU utilization (workload) only. Hence, we model the workload and the power consumption of a multicore processor as random variables and exploit the monotonicity property of their distribution functions to establish a quantitative relationship between the random variables. We will show that for a single-core processor the relationship is best approximated by a quadratic function whereas for a dualcore processor, the relationship is best approximated by a linear function. We will demonstrate the plausibility of our approach by estimating the power consumption of both custom-made and standard benchmarks (namely, the SPEC power benchmark and the Apache benchmarking tool) for an Intel and AMD processors.
关键词： power model,processor power,multicore processor,stochastic model,processor power consumption estimation,DC power consumption,processor workload analysis
更新于2025-09-23 15:19:57
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[IEEE 2019 IEEE Conference on Antenna Measurements & Applications (CAMA) - Kuta, Bali, Indonesia (2019.10.23-2019.10.25)] 2019 IEEE Conference on Antenna Measurements & Applications (CAMA) - 16-Elements Helical Antenna System Integration with a Solar Cell Powered IoT Collector
摘要： Quantitatively estimating the relationship between the workload and the corresponding power consumption of a multicore processor is an essential step towards achieving energy proportional computing. Most existing and proposed approaches use Performance Monitoring Counters (Hardware Monitoring Counters) for this task. In this paper we propose a complementary approach that employs the statistics of CPU utilization (workload) only. Hence, we model the workload and the power consumption of a multicore processor as random variables and exploit the monotonicity property of their distribution functions to establish a quantitative relationship between the random variables. We will show that for a single-core processor the relationship is best approximated by a quadratic function whereas for a dualcore processor, the relationship is best approximated by a linear function. We will demonstrate the plausibility of our approach by estimating the power consumption of both custom-made and standard benchmarks (namely, the SPEC power benchmark and the Apache benchmarking tool) for an Intel and AMD processors.
关键词： processor power,multicore processor,power model,processor workload analysis,stochastic model,processor power consumption estimation,DC power consumption
更新于2025-09-23 15:19:57