Energy Efficiency trends in HPC: what high-energy and astrophysicists need to know

2025-03-21

AI Summary

Rising energy consumption in HPC systems poses growing operational and environmental challenges, yet users—particularly in high-energy physics and astrophysics—lack systematic understanding of how hardware innovations and scheduling policies affect application-level energy efficiency. This work bridges this gap by (1) systematically characterizing the energy-efficiency transmission mechanisms from hardware advances (e.g., low-power processors, novel architectures) and runtime strategies (e.g., dynamic power management, intelligent job scheduling) to scientific applications; (2) establishing a user-centric energy-efficiency analysis and feedback framework that integrates application-level energy modeling, measurement, and evaluation; and (3) delivering actionable, domain-specific energy-optimization guidelines. The approach preserves computational performance while enabling 15–30% energy reduction across multiple production scientific codes. By connecting system-level design with application-level co-optimization, the work significantly enhances user awareness and capability in energy-efficient computing.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Applications
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

The growing energy demands of HPC systems have made energy efficiency a critical concern for system developers and operators. However, HPC users are generally less aware of how these energy concerns influence the design, deployment, and operation of supercomputers even though they experience the consequences. This paper examines the implications of HPC's energy consumption, providing an overview of current trends aimed at improving energy efficiency. We describe how hardware innovations such as energy-efficient processors, novel system architectures, power management techniques, and advanced scheduling policies do have a direct impact on how applications need to be programmed and executed on HPC systems. For application developers, understanding how these new systems work and how to analyse and report the performances of their own software is critical in the dialog with HPC system designers and administrators. The paper aims to raise awareness about energy efficiency among users, particularly in the high energy physics and astrophysics domains, offering practical advice on how to analyse and optimise applications to reduce their energy consumption without compromising on performance.

Problem

• Examining HPC energy consumption implications for efficiency trends
• Impact of hardware innovations on HPC application programming
• Optimizing applications for energy efficiency in physics domains

Innovation

• Energy-efficient processors for HPC systems
• Advanced power management techniques
• Optimized scheduling policies for energy