A processor power modeling accuracy improvement method based on static and dynamic sample point reconstruction.

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Bibliographic Details
Title:	A processor power modeling accuracy improvement method based on static and dynamic sample point reconstruction.
Authors:	ZHONG, Jiaqing¹ zhongjiaqing23@nudt.edu.cn, CHEN, Juan¹ juanchen@nudt.edu.cn, ZHOU, Yichang¹ zychang2023@nudt.edu.cn, WU, Xianyu¹ wuxianyu23@nudt.edu.cn, WANG, Rui¹ 874947622@qq.com, YU, Xiang¹ yuxiang228@foxmail.com
Source:	Computer Engineering & Science / Jisuanji Gongcheng yu Kexue. Dec2025, Vol. 47 Issue 12, p2108-2118. 11p.
Subjects:	Multicore processors, Benchmark problems (Computer science), Program transformation, Image reconstruction, Calibration
Abstract:	Establishing a high-precision, fine-grained CPU power consumption model is crucial for power management and optimization in computer systems. Addressing challenges such as the imbalance in the quantity and type distribution of modeling datasets in multi-core processor modeling, this paper proposes a method to enhance processor modeling accuracy based on the reconstruction of static and dynamic program sample points. Program samples are composed of data collected by performance monitoring counters (PMCs) during program execution. The static reconstruction algorithm reconstructs program sample points from three dimensions: Feature selection, time granularity refinement, and spatial redundancy reduction. As a complement to the static reconstruction algorithm, the dynamic reconstruction algorithm focuses on the behavior of programs running under various optimization techniques, such as different compilation options or varying resource loads. It selects program samples optimized with appropriate techniques to supplement the program sample points. To evaluate the impact of the static and dynamic sample point reconstruction algorithms on power modeling, this paper assesses five program benchmark suites on x86 and ARM processor platforms. The experimental results show that on two x86 platforms, when the power consumption models employ linear model, neural network model, and random forest model respectively, the average accuracy improvements are 74.80%, 65.70%, and 32.24%, as well as 61.61%, 80.44%, and 18.76%. On the ARM platform, the average accuracy improvements for linear model, neural network model, and random forest model are 22.34%, 34.63%, and 34.36%, respectively. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	Establishing a high-precision, fine-grained CPU power consumption model is crucial for power management and optimization in computer systems. Addressing challenges such as the imbalance in the quantity and type distribution of modeling datasets in multi-core processor modeling, this paper proposes a method to enhance processor modeling accuracy based on the reconstruction of static and dynamic program sample points. Program samples are composed of data collected by performance monitoring counters (PMCs) during program execution. The static reconstruction algorithm reconstructs program sample points from three dimensions: Feature selection, time granularity refinement, and spatial redundancy reduction. As a complement to the static reconstruction algorithm, the dynamic reconstruction algorithm focuses on the behavior of programs running under various optimization techniques, such as different compilation options or varying resource loads. It selects program samples optimized with appropriate techniques to supplement the program sample points. To evaluate the impact of the static and dynamic sample point reconstruction algorithms on power modeling, this paper assesses five program benchmark suites on x86 and ARM processor platforms. The experimental results show that on two x86 platforms, when the power consumption models employ linear model, neural network model, and random forest model respectively, the average accuracy improvements are 74.80%, 65.70%, and 32.24%, as well as 61.61%, 80.44%, and 18.76%. On the ARM platform, the average accuracy improvements for linear model, neural network model, and random forest model are 22.34%, 34.63%, and 34.36%, respectively. [ABSTRACT FROM AUTHOR]
ISSN:	1007130X
DOI:	10.3969/j.issn.1007-130X.2025.12.003