基于训练集聚类选择优化的CPU功耗建模精度提升方法  

CPU Power Modeling Accuracy Improvement Method Based on Training Set Clustering Selection

作　　者：李泽锴 钟佳卿 冯绍骏 陈娟[1] 邓荣宇 徐涛 谭政源 周柯杏 朱鹏志 马兆阳 LI Zekai;ZHONG Jiaqing;FENG Shaojun;CHEN Juan;DENG Rongyu;XU Tao;TAN Zhengyuan;ZHOU Kexing;ZHU Pengzhi;MA Zhaoyang(College of Computer Science and Technology,National University of Defense Technology,Changsha 410073,China)

机构地区：[1]国防科技大学计算机学院,长沙410073

出　　处：《计算机科学》2024年第9期59-70,共12页Computer Science

摘　　要：建立高精度、低开销的CPU功耗模型对于计算机系统的功耗管理与功耗优化至关重要。一般认为训练集规模越大,CPU功耗模型精度越高。但有研究发现增大训练集规模不一定会提高功耗建模精度,有时甚至会导致精度下降,因此,如何选择功耗模型训练集以保证CPU功耗模型精度达到要求具有重要意义。文中提出一种基于聚类的训练集选择优化算法来解决上述问题,在有效保证CPU功耗建模精度的同时降低了CPU功耗建模的开销。该算法首先通过主成分分析将基于PMC的程序特征转换为p维向量特征空间,然后根据找到的最优聚类数按照程序特征对程序进行聚类,从每个聚类簇中选出代表程序;最后根据“单聚类簇内代表性最强原则”与“多聚类簇间代表程序数最少原则”形成最优训练集,模型精度相比Baseline精度有明显提高。在x86和ARM两类处理器平台上分别采用线性功耗建模和神经网络功耗建模两种方式,对算法进行了实验评估,实验结果表明所提算法的功耗建模精度有效显著提升。Building a high-precision and low-cost CPU power model is crucial for power management and power optimization of computer systems.It is generally believed that the larger the size of the training set,the higher the accuracy of the CPU power model.However,some studies have found that increasing the size of the training set may not necessarily improve the accuracy of power modeling,or even sometimes leading to a decrease in accuracy.Therefore,it is necessary to screen the training set of the power model to ensure that the accuracy of the CPU power model does not decrease while achieving a low-cost target for model training.This paper proposes an optimization algorithm for training set selection based on clustering.It first converts PMC-based program features into a p-dimension vector feature space through principal component analysis(PCA),then clusters the programs according to the optimal number of clusters found,and selects representative programs from each cluster.Finally,according to the principle of selecting the strongest representative program within a single cluster and selecting the least number of representative programs among multiple clusters,a low-cost training set is achieved for a significant reduction in training overhead without loss of modeling accuracy.Experimental evaluation of the algorithm is conducted on both x86 and ARM-based processor platforms using linear power modeling and neural network power modeling,and the experimental results validate the effectiveness of the algorithm.These results indicate a significant improvement in CPU power consumption model accuracy.

关 键 词：CP功耗建模 训练集选择 主成分分析 K-MEANS聚类 

分 类 号：TP302[自动化与计算机技术—计算机系统结构]