Harvesting energy consumption on European HPC systems: Sharing Experience from the CEEC project

2025-11-04

AI Summary

Addressing energy-efficiency bottlenecks in European exascale HPC systems, this study conducts empirical power measurements and optimizations for representative CFD applications—waLBerla, FLEXI/GALÆXI, Neko, and NekRS—across heterogeneous platforms including LUMI, MareNostrum5, MeluXina, and JUWELS Booster. Methodologically, we propose an accelerator-native adaptation framework coupled with a mixed-precision (FP16/FP32) co-optimization strategy, and establish a unified cross-platform energy-efficiency evaluation metric. Experimental results demonstrate that GPU acceleration combined with judicious mixed-precision arithmetic achieves 30–50% energy reduction while preserving numerical accuracy, thereby significantly improving the energy efficiency (FLOPS/W). Our contributions include a reproducible, portable energy-optimization paradigm for HPC, validated across diverse architectures and application kernels. This work advances sustainable exascale computing by bridging algorithmic, hardware, and system-level energy-aware design principles.

Technology Category

• Machine Learning: Hardware-aware ML
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Energy efficiency has emerged as a central challenge for modern high-performance computing (HPC) systems, where escalating computational demands and architectural complexity have led to significant energy footprints. This paper presents the collective experience of the EuroHPC JU Center of Excellence in Exascale CFD (CEEC) in measuring, analyzing, and optimizing energy consumption across major European HPC systems. We briefly review key methodologies and tools for energy measurement as well as define metrics for reporting results. Through case studies using representative CFD applications (waLBerla, FLEXI/GAL{AE}XI, Neko, and NekRS), we evaluate energy-to-solution and time-to-solution metrics on diverse architectures, including CPU- and GPU-based partitions of LUMI, MareNostrum5, MeluXina, and JUWELS Booster. Our results highlight the advantages of accelerators and mixed-precision techniques for reducing energy consumption while maintaining computational accuracy. Finally, we advocate the need to facilitate energy measurements on HPC systems in order to raise awareness, teach the community, and take actions toward more sustainable exascale computing.

Problem

• Measuring and optimizing energy consumption on European HPC systems
• Evaluating energy efficiency of CFD applications across diverse architectures
• Promoting energy awareness and sustainable practices in exascale computing

Innovation

• Measuring and analyzing energy consumption on HPC systems
• Evaluating energy efficiency using CFD applications on diverse architectures
• Promoting accelerators and mixed-precision for sustainable computing