Machine Learning for Generic Energy Models of High Performance Computing Resources

This article presents an study of the generalization capabilities of forecasting techniques of empirical energy consumption models of high performance computing resources. This is a relevant subject, considering the large energy utilization of modern supercomputing facilities. Different energy models are built, considering several forecasting techniques and using information from the execution of a benchmark over different hardware. A cross-evaluation is performed and the training information of each model is gradually extended with information about other hardware. Each model is analyzed to evaluate how new information impacts on the prediction capabilities. The main results indicate that neural network approaches achieve the highest quality results when the training data of the models is expanded with minimal information from new scenarios.