Work

Measuring Sustainability in Multi-Scale High-Performance Computing

Year: 2026

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2609.08688

Authors: Barrios, Carlos J, Mouël, Frédéric Le, Denneulin, Yves

Institutions: Institut National des Sciences Appliquées de Lyon, DATAMOVE: Mouvements de données pour le calcul haute performance, Université Grenoble Alpes

Language: English

Topic: Cloud Computing and Resource Management

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Institut national de recherche en informatique et en automatique (INRIA), Centre National de la Recherche Scientifique

OpenAlex ID: W7212044690

Cites:

Cited by:

Related to: