Enabling Demand Response for HPC Systems through Power Capping and Node Scaling

Demand response is an increasingly popular program ensuring power grid stability during a sudden surge in power demand. We expect high-performance computing (HPC) systems to be valued participants in such program for their massive power consumption. In this paper, we propose an emergency demand-response model exploiting both power capping of HPC systems and node scaling of HPC applications. First, we present power and performance prediction models for HPC systems with only power capping, upon which we propose our demand-response model. We validate the models with real-life measurements of application characteristics. Next, we present models to predict energy-to-solution for HPC applications with different numbers of nodes and power-capping values, and we validate the models. Based on the prediction models, we propose an emergency demand response participation model for HPC systems to determine optimal resource allocation based on power capping and node scaling. Finally, we demonstrate the effectiveness of our proposed demand-response model using real-life measurements and trace data. We show that our approach can reduce energy consumption with only a slight increase in the execution time for HPC applications during critical demand response periods.