计算机工程与科学2025，Vol.47Issue(12)：2108-2118,11.DOI:10.3969/j.issn.1007-130X.2025.12.003
基于静动态样本点重构的处理器功耗建模精度提升方法
A processor power modeling accuracy improvement method based on static and dynamic sample point reconstruction
ZHONG Jiaqing 1CHEN Juan 1ZHOU Yichang 1WU Xianyu 1WANG Rui 1YU Xiang1
作者信息
- 1. College of Computer Science and Technology,National University of Defense Technology,Changsha 410073,China
- 折叠
摘要
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 dy-namic program sample points.Program samples are composed of data collected by performance monitor-ing counters(PMCs)during program execution.The static reconstruction algorithm reconstructs pro-gram sample points from three dimensions:Feature selection,time granularity refinement,and spatial redundancy reduction.As a complement to the static reconstruction algorithm,the dynamic reconstruc-tion 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 ap-propriate 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 im-provements for linear model,neural network model,and random forest model are 22.34%,34.63%,and 34.36%,respectively.关键词
样本点重构/静动态结合/CPU功耗建模/训练集优化/高精度Key words
sample point reconstruction/static-dynamic integration/CPU power modeling/training set optimization/high precision分类
信息技术与安全科学引用本文复制引用
ZHONG Jiaqing,CHEN Juan,ZHOU Yichang,WU Xianyu,WANG Rui,YU Xiang..基于静动态样本点重构的处理器功耗建模精度提升方法[J].计算机工程与科学,2025,47(12):2108-2118,11.
