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arXiv · 2607.19150

Job-level Carbon and Water Footprint Estimation for HPC: Bias Assessment from Runtime to Full Life Cycle

Xi Chen·Chris Broekema·Rob van Nieuwpoort
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Abstract

High performance computing evaluation has traditionally focused on performance and energy, but these metrics alone cannot capture the sustainability cost of runtime configurations. We proposes a unified job-level water and carbon accounting framework with both operational and embodied impacts. Results show that higher thread counts generally reduce total footprint, but the benefit diminishes at higher thread counts. Water is mainly dominated by embodied impact, whereas carbon is mainly dominated by operational impact.

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Xi Chen, Chris Broekema, Rob van Nieuwpoort. 2026-07-21. Job-level Carbon and Water Footprint Estimation for HPC: Bias Assessment from Runtime to Full Life Cycle. https://arxiv.org/abs/2607.19150

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