Modeling Power Consumption of The Code Execution Using Performance Counters Statistics

Abstract

This paper presents an empirical model to classify the programs according to their power consumption by using the performance counter statistics. The programs with similar power consumption are put into the same group. The difference in power data between two adjacent groups is 5 watts. A power model is generated based on the performance data that the program generated. Discriminant analysis is adopted to generate the power consumption model upon the data from the performance counter statistics. We use discriminant analysis to determine the power category (i.e., the number of the group) that is derived from the independent variable. By using the performance counter variables as the input to the power model, we can predict the level of power consumption of the code, that is, the group that this code belongs to. The experiment results in modeling and validation show that this power model can predict power group membership of a code with an accuracy of more than 96.5%, with the difference of original and predicted group numbers being smaller than 2.