Sustainable-HPC: toward Digital Twin for active management of self-cooled data centers with Renewable Energy Sources and waste heat recovery

Authors: S. Meschini, L. C. Tagliabue, G. M. D. Giuda, M. Aldinucci, P. Gasbarri and D. Accardo.

Abstract: High-performance computing (HPC) data centers are key for advancing research and industry, but their high energy consumption and environmental impact pose critical sustainability challenges. Few HPC centers currently use renewable energy sources, particularly hydrogen-based storage, or integrate waste heat reuse. Recently, Digital Twins (DTs) are proving crucial for advancing sustainable HPC centers, enabling the integration of advanced monitoring and energy optimization with renewable energy sources. They simulate operations, predict maintenance needs, balance workloads, and support renewable integration for enhanced efficiency. The paper first reviews current practices, focusing on the potential of DTs to improve HPC sustainability by dynamically managing resources and optimizing systems, including cooling, power distribution, and load balancing. Then, it illustrates the University of Turin’s Sustainable HPC4AI (S-HPC4AI) project, which aims to develop a low impact HPC center to support Artificial Intelligence (AI) research across diverse scientific fields. The facility will feature renewable energy sources, advanced self-cooling, and waste heat recovery, setting a benchmark for energy-efficient, low-carbon HPC infrastructure. Central to the project is the integration of hydrogen and solar energy, with photovoltaic systems providing clean power and hydrogen fuel cells serving as reliable backup sources, reducing reliance on fossil fuels. Waste heat can be transformed into a productive resource for a local automated phenotyping system, reducing energy consumption and environmental impact on the overall. Furthermore, a DT will be developed, integrating BIM with sensors data about performance, resource usage and operating conditions, enabling real-time monitoring and predictive analytics for managing power, cooling, and energy resources. The potential and challenges of the S-HPC4AI model are discussed, suggesting possible solutions.

Keywords: Data center, HPC, DT, sustainability, efficiency, BIM, liquid cooling, hydrogen, RES, monitoring.

This article can also be downloaded from the publishers website: https://ieeexplore.ieee.org/document/10974817