{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,5]],"date-time":"2026-07-05T00:18:31Z","timestamp":1783210711283,"version":"3.54.6"},"reference-count":27,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100022963","name":"Key Research and Development Program of Zhejiang Province","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100022963","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Sustainable Computing: Informatics and Systems"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.suscom.2026.101415","type":"journal-article","created":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T15:34:06Z","timestamp":1782920046000},"page":"101415","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A predictive offline\u2013online hybrid approach to DNN training energy optimization"],"prefix":"10.1016","volume":"51","author":[{"given":"Ling","family":"He","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongni","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Congfeng","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junming","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuanqi","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liangbin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"3","key":"10.1016\/j.suscom.2026.101415_b1","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1145\/3630614.3630626","article-title":"Treehouse: A case for carbon-aware datacenter software","volume":"3","author":"Anderson","year":"2023","journal-title":"ACM SIGENERGY Energy Informatics Rev."},{"key":"10.1016\/j.suscom.2026.101415_b2","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1016\/j.future.2023.03.041","article-title":"Dynamic GPU power capping with online performance tracing for energy efficient GPU computing using DEPO tool","volume":"145","author":"Krzywaniak","year":"2023","journal-title":"Future Gener. Comput. Syst."},{"issue":"2","key":"10.1016\/j.suscom.2026.101415_b3","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1109\/LCA.2023.3278652","article-title":"Towards improved power management in cloud gpus","volume":"22","author":"Patel","year":"2023","journal-title":"IEEE Comput. Archit. Lett."},{"key":"10.1016\/j.suscom.2026.101415_b4","series-title":"Multi-objective optimization approach using deep reinforcement learning for energy efficiency in heterogeneous computing system","author":"Yu","year":"2023"},{"key":"10.1016\/j.suscom.2026.101415_b5","unstructured":"J. You, J.-W. Chung, M. Chowdhury, Zeus: Understanding and optimizing {GPU} energy consumption of {DNN} training, in: 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 23, 2023, pp. 119\u2013139."},{"key":"10.1016\/j.suscom.2026.101415_b6","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.comcom.2022.10.019","article-title":"An energy-optimized embedded load balancing using DVFS computing in cloud data centers","volume":"197","author":"Javadpour","year":"2023","journal-title":"Comput. Commun."},{"issue":"3","key":"10.1016\/j.suscom.2026.101415_b7","doi-asserted-by":"crossref","first-page":"868","DOI":"10.1109\/TPDS.2014.2315203","article-title":"Power-aware job scheduling on heterogeneous multicore architectures","volume":"26","author":"Chiesi","year":"2014","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"10.1016\/j.suscom.2026.101415_b8","series-title":"Improving Energy Efficiency of Basic Linear Algebra Routines on Heterogeneous Systems With Multiple GPUs","author":"Zamani Sabzi","year":"2022"},{"issue":"2","key":"10.1016\/j.suscom.2026.101415_b9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3583590","article-title":"GreenMD: Energy-efficient matrix decomposition on heterogeneous multi-GPU systems","volume":"10","author":"Zamani","year":"2023","journal-title":"ACM Trans. Parallel Comput."},{"key":"10.1016\/j.suscom.2026.101415_b10","series-title":"2023 Forum on Specification & Design Languages","first-page":"1","article-title":"Hybrid PTX analysis for GPU accelerated CNN inferencing aiding computer architecture design","author":"Metz","year":"2023"},{"key":"10.1016\/j.suscom.2026.101415_b11","series-title":"Energy-efficient GPU clusters scheduling for deep learning","author":"Gu","year":"2023"},{"key":"10.1016\/j.suscom.2026.101415_b12","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.jpdc.2022.09.002","article-title":"Evaluating execution time predictions on gpu kernels using an analytical model and machine learning techniques","volume":"171","author":"Amaris","year":"2023","journal-title":"J. Parallel Distrib. Comput."},{"key":"10.1016\/j.suscom.2026.101415_b13","series-title":"2020 20th IEEE\/ACM International Symposium on Cluster, Cloud and Internet Computing","first-page":"559","article-title":"Indicator-directed dynamic power management for iterative workloads on GPU-accelerated systems","author":"Zou","year":"2020"},{"key":"10.1016\/j.suscom.2026.101415_b14","doi-asserted-by":"crossref","unstructured":"S. Bharadwaj, S. Das, Y. Eckert, M. Oskin, T. Krishna, DUB: Dynamic underclocking and bypassing in nocs for heterogeneous GPU workloads, in: Proceedings of the 15th IEEE\/ACM International Symposium on Networks-on-Chip, 2021, pp. 49\u201354.","DOI":"10.1145\/3479876.3481590"},{"key":"10.1016\/j.suscom.2026.101415_b15","doi-asserted-by":"crossref","unstructured":"Y. Zhang, Q. Wang, Z. Lin, P. Xu, B. Wang, Improving GPU energy efficiency through an application-transparent frequency scaling policy with performance assurance, in: Proceedings of the Nineteenth European Conference on Computer Systems, 2024, pp. 769\u2013785.","DOI":"10.1145\/3627703.3629584"},{"issue":"20","key":"10.1016\/j.suscom.2026.101415_b16","doi-asserted-by":"crossref","DOI":"10.1002\/cpe.7285","article-title":"Efficient exact algorithms for continuous bi-objective performance-energy optimization of applications with linear energy and monotonically increasing performance profiles on heterogeneous high performance computing platforms","volume":"35","author":"Khaleghzadeh","year":"2023","journal-title":"Concurr. Comput.: Pract. Exp."},{"key":"10.1016\/j.suscom.2026.101415_b17","doi-asserted-by":"crossref","first-page":"63149","DOI":"10.1109\/ACCESS.2021.3075139","article-title":"Energy predictive models of computing: theory, practical implications and experimental analysis on multicore processors","volume":"9","author":"Shahid","year":"2021","journal-title":"IEEE Access"},{"issue":"4","key":"10.1016\/j.suscom.2026.101415_b18","doi-asserted-by":"crossref","first-page":"248","DOI":"10.3390\/info14040248","article-title":"Energy-efficient parallel computing: Challenges to scaling","volume":"14","author":"Lastovetsky","year":"2023","journal-title":"Information"},{"issue":"2","key":"10.1016\/j.suscom.2026.101415_b19","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1109\/TC.2017.2742513","article-title":"Bi-objective optimization of data-parallel applications on homogeneous multicore clusters for performance and energy","volume":"67","author":"Manumachu","year":"2017","journal-title":"IEEE Trans. Comput."},{"issue":"4","key":"10.1016\/j.suscom.2026.101415_b20","doi-asserted-by":"crossref","DOI":"10.1002\/cpe.4958","article-title":"Design of self-adaptable data parallel applications on multicore clusters automatically optimized for performance and energy through load distribution","volume":"31","author":"Reddy Manumachu","year":"2019","journal-title":"Concurr. Comput.: Pract. Exp."},{"issue":"3","key":"10.1016\/j.suscom.2026.101415_b21","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1109\/TPDS.2020.3027338","article-title":"Bi-objective optimization of data-parallel applications on heterogeneous HPC platforms for performance and energy through workload distribution","volume":"32","author":"Khaleghzadeh","year":"2020","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"10.1016\/j.suscom.2026.101415_b22","doi-asserted-by":"crossref","unstructured":"F. Guo, Y. Li, J.C. Lui, Y. Xu, Dcuda: Dynamic gpu scheduling with live migration support, in: Proceedings of the ACM Symposium on Cloud Computing, 2019, pp. 114\u2013125.","DOI":"10.1145\/3357223.3362714"},{"key":"10.1016\/j.suscom.2026.101415_b23","series-title":"Euro-Par 2017: Parallel Processing Workshops: Euro-Par 2017 International Workshops, Santiago de Compostela, Spain, August 28-29, 2017, Revised Selected Papers 23","first-page":"353","article-title":"Approximation algorithm for scheduling a chain of tasks on heterogeneous systems","author":"Ait Aba","year":"2018"},{"issue":"6","key":"10.1016\/j.suscom.2026.101415_b24","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.1109\/TPDS.2015.2456020","article-title":"Energy and makespan tradeoffs in heterogeneous computing systems using efficient linear programming techniques","volume":"27","author":"Tarplee","year":"2015","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"10.1016\/j.suscom.2026.101415_b25","series-title":"International Conference on High Performance Computing","first-page":"413","article-title":"Metrics for energy-aware software optimisation","author":"Roberts","year":"2017"},{"issue":"3","key":"10.1016\/j.suscom.2026.101415_b26","doi-asserted-by":"crossref","first-page":"1545","DOI":"10.1007\/s12065-021-00565-2","article-title":"Precision\u2013recall curve (PRC) classification trees","volume":"15","author":"Miao","year":"2022","journal-title":"Evol. Intell."},{"issue":"11","key":"10.1016\/j.suscom.2026.101415_b27","first-page":"2943","article-title":"Dynamic GPU energy optimization for machine learning training workloads","volume":"33","author":"Wang","year":"2021","journal-title":"IEEE Trans. Parallel Distrib. Syst."}],"container-title":["Sustainable Computing: Informatics and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2210537926001253?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2210537926001253?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T23:59:25Z","timestamp":1783209565000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2210537926001253"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":27,"alternative-id":["S2210537926001253"],"URL":"https:\/\/doi.org\/10.1016\/j.suscom.2026.101415","relation":{},"ISSN":["2210-5379"],"issn-type":[{"value":"2210-5379","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A predictive offline\u2013online hybrid approach to DNN training energy optimization","name":"articletitle","label":"Article Title"},{"value":"Sustainable Computing: Informatics and Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.suscom.2026.101415","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"101415"}}