{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T14:34:56Z","timestamp":1762958096335,"version":"3.45.0"},"reference-count":11,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T00:00:00Z","timestamp":1762905600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T00:00:00Z","timestamp":1762905600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100003708","name":"Korea Institute of Science and Technology Information","doi-asserted-by":"publisher","award":["No. K25L2M1C1","No. K25L2M1C1"],"award-info":[{"award-number":["No. K25L2M1C1","No. K25L2M1C1"]}],"id":[{"id":"10.13039\/501100003708","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Energy Inform"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats: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.<\/jats:p>","DOI":"10.1186\/s42162-025-00586-6","type":"journal-article","created":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T14:30:45Z","timestamp":1762957845000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Quantifying job-level carbon efficiency in HPC: an empirical study based on the PM100 dataset"],"prefix":"10.1186","volume":"8","author":[{"given":"Hyungwook","family":"Shim","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,12]]},"reference":[{"key":"586_CR1","doi-asserted-by":"publisher","DOI":"10.1145\/3600006.3613167","author":"J Wu","year":"2023","unstructured":"Wu J, Liu S, Sun L, Hou T, Zhou X, Ren K, Zheng Z (2023) Greenflow: carbon-aware GPU scheduling for deep learning training. 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IEEE Syst J 13(4):3873\u20133876","journal-title":"IEEE Syst J"},{"key":"586_CR11","doi-asserted-by":"publisher","unstructured":"Antici F, Seyedkazemi Ardebili M, Bartolini A, Kiziltan Z (2023) PM100: A Job Power Consumption Dataset of a Large-Scale HPC System [Data set]. In Proceedings of the SC \u201823 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis. Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis (SC-W 23), Denver. 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