{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T09:45:53Z","timestamp":1782899153906,"version":"3.54.5"},"reference-count":50,"publisher":"IEEE","license":[{"start":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T00:00:00Z","timestamp":1779667200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T00:00:00Z","timestamp":1779667200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,5,25]]},"DOI":"10.1109\/ipdps65963.2026.00100","type":"proceedings-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T20:55:16Z","timestamp":1782852916000},"page":"1204-1218","source":"Crossref","is-referenced-by-count":0,"title":["PowerMorph: Shaping LLM Training for Data Center Demand Response"],"prefix":"10.1109","author":[{"given":"Boqiang","family":"Li","sequence":"first","affiliation":[{"name":"Clemson University,Clemson,South Carolina"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luanzheng","family":"Guo","sequence":"additional","affiliation":[{"name":"Pacific Northwest National Laboratory,Richland,Washington"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Buxin","family":"She","sequence":"additional","affiliation":[{"name":"Pacific Northwest National Laboratory,Richland,Washington"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nathan R.","family":"Tallent","sequence":"additional","affiliation":[{"name":"Pacific Northwest National Laboratory,Richland,Washington"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Veronica","family":"Adetola","sequence":"additional","affiliation":[{"name":"Pacific Northwest National Laboratory,Richland,Washington"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rong","family":"Ge","sequence":"additional","affiliation":[{"name":"Clemson University,Clemson,South Carolina"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","volume-title":"Generative ai is exhausting the power grid","author":"Shukla","year":"2024"},{"key":"ref2","article-title":"AI is set to drive surging electricity demand from data centres while offering the potential to transform how the energy sector works","year":"2025"},{"key":"ref3","article-title":"Connection requirements for transmission-connected data centres","year":"2025"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/2018536.2018541"},{"key":"ref5","article-title":"Power costs soar in pjm region as data center demand spikes","author":"Kearney","year":"2025"},{"key":"ref6","article-title":"Microsoft data centre tries out \u2019grid-interactive UPS\u2019 battery storage","volume-title":"Energy-Storage.news","author":"Colthorpe","year":"2022"},{"key":"ref7","article-title":"Plug power to see demand for hydrogen-based power backup systems from data centers","volume-title":"Reuters","author":"Dareen","year":"2024"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/2465529.2465740"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3575813.3595197"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.adapen.2024.100202"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3731569.3764839"},{"key":"ref12","article-title":"Our data centers now work harder when the sun shines and wind blows","volume-title":"Google Blog \u2014 The Keyword","author":"Radovanovic","year":"2020"},{"key":"ref13","article-title":"We now do more computing where there\u2019s cleaner energy","volume-title":"Google Blog \u2014 The Keyword","author":"Koningstein","year":"2021"},{"key":"ref14","article-title":"Beyond pue: Flexible datacenters empowering the cloud to decarbonize","author":"Chien","year":"2022","journal-title":"USENIX Hot Carbon"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2023.03.041"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-08751-6_48"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TSUSC.2024.3362697"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2024.3470074"},{"key":"ref19","article-title":"Energy-efficient gpu clusters scheduling for deep learning","author":"Gu","year":"2023"},{"key":"ref20","first-page":"119","article-title":"Zeus: Understanding and optimizing {GPU} energy consumption of {DNN} training","volume-title":"20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23)","author":"You"},{"key":"ref21","article-title":"Opportunities of renewable energy powered dnn inference","author":"Nabavinejad","year":"2023"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2018.2816808"},{"key":"ref23","volume-title":"PJM Manual 12: Balancing Operations","year":"2025"},{"issue":"2","key":"ref24","first-page":"3","article-title":"Lora: Low-rank adaptation of large language models","volume-title":"ICLR","volume":"1","author":"Hu"},{"key":"ref25","article-title":"Mixed precision training","author":"Micikevicius","year":"2017"},{"key":"ref26","article-title":"Pollux: Co-adaptive cluster scheduling for goodput-optimized deep learning","volume-title":"15th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 21)","author":"Qiao"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.52202\/068431-1189"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/SC41405.2020.00024"},{"key":"ref29","first-page":"16 639","article-title":"Bpipe: Memory-balanced pipeline parallelism for training large language models","volume-title":"International Conference on Machine Learning","author":"Kim"},{"key":"ref30","article-title":"Deepseek-v3 technical report","author":"Liu","year":"2024"},{"key":"ref31","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Artificial intelligence and statistics","author":"McMahan","year":"2017"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/1815961.1815998"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/2830772.2830826"},{"key":"ref34","article-title":"calflops: a flops and params calculate tool for neural networks in pytorch framework","year":"2023"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01696-6"},{"key":"ref36","first-page":"3","article-title":"Qwen2 technical report","volume":"2","year":"2024"},{"key":"ref37","article-title":"The llama 3 herd of models","author":"Grattafiori","year":"2024"},{"key":"ref38","article-title":"Qwen2. 5 technical report","author":"Yang","year":"2024"},{"key":"ref39","article-title":"Databricks dolly 15k","year":"2025"},{"key":"ref40","article-title":"Open-platypus","year":"2025"},{"key":"ref41","article-title":"orcachat","year":"2025"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ICCD.2013.6657064"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA.2017.34"},{"key":"ref44","article-title":"Eaco: Resource sharing dynamics and its impact on energy efficiency for dnn training","author":"Haghshenas","year":"2024"},{"key":"ref45","first-page":"119","article-title":"Zeus: Understanding and optimizing {GPU} energy consumption of {DNN} training","volume-title":"20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23)","author":"You"},{"key":"ref46","first-page":"551","article-title":"{Zero-offload}: Democratizing {billion-scale} model training","volume-title":"2021 USENIX Annual Technical Conference (USENIX ATC 21)","author":"Ren"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1145\/3458817.3476205"},{"key":"ref48","article-title":"Zero++: Extremely efficient collective communication for giant model training","author":"Wang","year":"2023"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3406703"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.14778\/3611540.3611569"}],"event":{"name":"2026 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","location":"New Orleans, LA, USA","start":{"date-parts":[[2026,5,25]]},"end":{"date-parts":[[2026,5,29]]}},"container-title":["2026 IEEE International Parallel and Distributed Processing Symposium (IPDPS)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11575315\/11575316\/11575428.pdf?arnumber=11575428","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T08:48:57Z","timestamp":1782895737000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11575428\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,25]]},"references-count":50,"URL":"https:\/\/doi.org\/10.1109\/ipdps65963.2026.00100","relation":{},"subject":[],"published":{"date-parts":[[2026,5,25]]}}}