Tag: HPC demand response

Enabling Demand Response for HPC Systems Through Power Capping and Node Scaling, Kishwar Ahmed, Jason Liu, and Kazutomo Yoshii. In Proceedings of the 20th IEEE International Conference on High Performance Computing and Communications (HPCC-2018), June 2018. [to appear]

Abstract

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.

Bibtex

@inproceedings{hpcc18-power,
title = {Enabling Demand Response for HPC Systems Through Power Capping and Node Scaling},
author = {Kishwar Ahmed and Jason Liu and Kazutomo Yoshii},
booktitle = {Proceedings of the 20th IEEE International Conference on High Performance Computing and Communications (HPCC'18)},
month = {June},
year = {2018}
}

An Energy Efficient Demand-Response Model for High Performance Computing Systems, Kishwar Ahmed, Jason Liu, and Xingfu Wu. In Proceedings of the 25th IEEE International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS 2017), September 2017.  [paper]

Abstract

Demand response refers to reducing energy consumption of participating systems in response to transient surge in power demand or other emergency events. Demand response is particularly important for maintaining power grid transmission stability, as well as achieving overall energy saving. High Performance Computing (HPC) systems can be considered as ideal participants for demand-response programs, due to their massive energy demand. However, the potential loss of performance must be weighed against the possible gain in power system stability and energy reduction. In this paper, we explore the opportunity of demand response on HPC systems by proposing a new HPC job scheduling and resource provisioning model. More specifically, the proposed model applies power-bound energy-conservation job scheduling during the critical demand-response events, while maintaining the traditional performance-optimized job scheduling during the normal period. We expect such a model can attract willing articipation of the HPC systems in the demand response programs, as it can improve both power stability and energy saving without significantly compromising application performance. We implement the proposed method in a simulator and compare it with the traditional scheduling approach. Using trace-driven simulation, we demonstrate that the HPC demand response is a viable approach toward power stability and energy savings with only marginal increase in the jobs’ execution time.

Bibtex

@inproceedings{mascots17-energy,
  title={An Energy Efficient Demand-Response Model for High Performance Computing Systems},
  author={Ahmed, Kishwar and Liu, Jason and Wu, Xingfu},
  booktitle={Proceedings of the 25th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS 2017)},
  pages={175--186},
  month={September},
  year={2017}
}

Slides