Energy-aware operation of HPC systems in Germany
| Autor: | Suarez, Estela, Bockelmann, Hendryk, Eicker, Norbert, Eitzinger, Jan, Sayed, Salem El, Fieseler, Thomas, Frank, Martin, Frech, Peter, Giesselmann, Pay, Hackenberg, Daniel, Hager, Georg, Herten, Andreas, Ilsche, Thomas, Koller, Bastian, Laure, Erwin, Manzano, Cristina, Oeste, Sebastian, Ott, Michael, Reuter, Klaus, Schneider, Ralf, Thust, Kay, Vieth, Benedikt von St. |
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| Rok vydání: | 2024 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: 30 pages, 3 figures, 4 tables |
| Databáze: | arXiv |
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