Measuring Sustainability in Multi-Scale High-Performance Computing

2026-09-08

AI Summary

本文通过引入一个多维度度量框架，解决多尺度高性能计算中的资源管理和可持续性问题，指导现代工作负载的部署策略。

Abstract

The transition from traditional High Performance Computing (HPC) to the Computing Continuum emphasizes efficient resource management and sustainable practices across Multi-Scale hybrid architectures. This paper introduces a multidimensional metric framework to characterize these systems and guide deployment strategies for modern workloads. The framework combines Architectural Performance metrics (such as Throughput, Latency, Scalability), System Utilization, and key Sustainability and Accuracy indicators (such as Energy Efficiency and Power Consumption). Using a modular hybrid testbed, experiments reveal complex relationships among metrics, especially the trade-offs between accuracy and energy, and the efficiency of hybrid nodes. The guidelines help identify optimal operating points and lay the groundwork for improving orchestrators and schedulers (e.g., Kubernetes) to assign demanding applications, including AI and Quantum Computing, to suitable system modules, ensuring high performance and sustainability.

Problem

• Sustainability
• High-Performance Computing
• Multi-Scale
• Resource Management
• Hybrid Architectures

Innovation

• multidimensional metric framework
• sustainability indicators
• hybrid architectures
• energy efficiency
• orchestrators and schedulers