Energy-aware operation of HPC systems in Germany
Abstract
High-Performance Computing (HPC) systems are among the most energy-intensive scientific facilities, with electric power consumption reaching and often exceeding 20 megawatts per installation. Unlike other major scientific infrastructures such as particle accelerators or high-intensity light sources, which are few around the world, the number and size of supercomputers are continuously increasing. Even if every new system generation is more energy efficient than the previous one, the overall growth in size of the HPC infrastructure, driven by a rising demand for computational capacity across all scientific disciplines, and especially by artificial intelligence workloads (AI), rapidly drives up the energy demand. This challenge is particularly significant for HPC centers in Germany, where high electricity costs, stringent national energy policies, and a strong commitment to environmental sustainability are key factors. This paper describes various state-of-the-art strategies and innovations employed to enhance the energy efficiency of HPC systems within the national context. Case studies from leading German HPC facilities illustrate the implementation of novel heterogeneous hardware architectures, advanced monitoring infrastructures, high-temperature cooling solutions, energy-aware scheduling, and dynamic power management, among other optimizations. By reviewing best practices and ongoing research, this paper aims to share valuable insight with the global HPC community, motivating the pursuit of more sustainable and energy-efficient HPC operations.
Cite
@article{arxiv.2411.16204,
title = {Energy-aware operation of HPC systems in Germany},
author = {Estela Suarez and Hendryk Bockelmann and Norbert Eicker and Jan Eitzinger and Salem El Sayed and Thomas Fieseler and Martin Frank and Peter Frech and Pay Giesselmann and Daniel Hackenberg and Georg Hager and Andreas Herten and Thomas Ilsche and Bastian Koller and Erwin Laure and Cristina Manzano and Sebastian Oeste and Michael Ott and Klaus Reuter and Ralf Schneider and Kay Thust and Benedikt von St. Vieth},
journal= {arXiv preprint arXiv:2411.16204},
year = {2024}
}
Comments
30 pages, 3 figures, 4 tables