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<p>This study utilizes the PM100 dataset to quantitatively estimate job-level carbon emissions and analyze efficiency across different resource configurations. A multilayer perceptron (MLP) regression model was applied to predict emissions using execution time along with CPU, memory, and node-level power consumption data. To evaluate efficiency, we proposed the Carbon Efficiency Score (CES), which enables the classification of jobs into efficiency tiers. The analysis revealed that long-running jobs with excessive memory usage tend to exhibit low efficiency, whereas jobs with balanced resource configurations demonstrate relatively higher efficiency. CES-based classification further showed a difference of more than 200-fold between the most and least efficient jobs. Overall, this study provides a foundational framework for developing carbon-aware scheduling strategies in HPC environments and offers practical insights for the design of sustainable supercomputing operational policies.</p>

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Quantifying job-level carbon efficiency in HPC: an empirical study based on the PM100 dataset

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月12日

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  • Hyungwook Shim

摘要

This study utilizes the PM100 dataset to quantitatively estimate job-level carbon emissions and analyze efficiency across different resource configurations. A multilayer perceptron (MLP) regression model was applied to predict emissions using execution time along with CPU, memory, and node-level power consumption data. To evaluate efficiency, we proposed the Carbon Efficiency Score (CES), which enables the classification of jobs into efficiency tiers. The analysis revealed that long-running jobs with excessive memory usage tend to exhibit low efficiency, whereas jobs with balanced resource configurations demonstrate relatively higher efficiency. CES-based classification further showed a difference of more than 200-fold between the most and least efficient jobs. Overall, this study provides a foundational framework for developing carbon-aware scheduling strategies in HPC environments and offers practical insights for the design of sustainable supercomputing operational policies.