CPU Power Modeling Accuracy Improvement Method Based on Training Set Clustering Selection
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 pro-grams 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 repre-sentative 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.
CPU power modelingTraining set selectionPrincipal component analysisK-means clustering
万方数据
维普
