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

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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]
Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A processor power modeling accuracy improvement method based on static and dynamic sample point reconstruction.
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  Data: <searchLink fieldCode="AR" term="%22ZHONG%2C+Jiaqing%22">ZHONG, Jiaqing</searchLink><relatesTo>1</relatesTo><i> zhongjiaqing23@nudt.edu.cn</i><br /><searchLink fieldCode="AR" term="%22CHEN%2C+Juan%22">CHEN, Juan</searchLink><relatesTo>1</relatesTo><i> juanchen@nudt.edu.cn</i><br /><searchLink fieldCode="AR" term="%22ZHOU%2C+Yichang%22">ZHOU, Yichang</searchLink><relatesTo>1</relatesTo><i> zychang2023@nudt.edu.cn</i><br /><searchLink fieldCode="AR" term="%22WU%2C+Xianyu%22">WU, Xianyu</searchLink><relatesTo>1</relatesTo><i> wuxianyu23@nudt.edu.cn</i><br /><searchLink fieldCode="AR" term="%22WANG%2C+Rui%22">WANG, Rui</searchLink><relatesTo>1</relatesTo><i> 874947622@qq.com</i><br /><searchLink fieldCode="AR" term="%22YU%2C+Xiang%22">YU, Xiang</searchLink><relatesTo>1</relatesTo><i> yuxiang228@foxmail.com</i>
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  Data: <searchLink fieldCode="JN" term="%22Computer+Engineering+%26+Science+%2F+Jisuanji+Gongcheng+yu+Kexue%22">Computer Engineering & Science / Jisuanji Gongcheng yu Kexue</searchLink>. Dec2025, Vol. 47 Issue 12, p2108-2118. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Multicore+processors%22">Multicore processors</searchLink><br /><searchLink fieldCode="DE" term="%22Benchmark+problems+%28Computer+science%29%22">Benchmark problems (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Program+transformation%22">Program transformation</searchLink><br /><searchLink fieldCode="DE" term="%22Image+reconstruction%22">Image reconstruction</searchLink><br /><searchLink fieldCode="DE" term="%22Calibration%22">Calibration</searchLink>
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  Label: Abstract
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  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3969/j.issn.1007-130X.2025.12.003
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      – Code: chi
        Text: Chinese
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        PageCount: 11
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      – SubjectFull: Benchmark problems (Computer science)
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      – SubjectFull: Program transformation
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      – SubjectFull: Image reconstruction
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      – SubjectFull: Calibration
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      – TitleFull: A processor power modeling accuracy improvement method based on static and dynamic sample point reconstruction.
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            NameFull: ZHONG, Jiaqing
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              M: 12
              Text: Dec2025
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