{"id":"https://openalex.org/W4399117356","doi":"https://doi.org/10.1145/3634769.3634806","title":"Unveiling the Truth: An Analysis of the Energy and Carbon Footprint of Training an OPT Model using DeepSpeed on the H100 GPU","display_name":"Unveiling the Truth: An Analysis of the Energy and Carbon Footprint of Training an OPT Model using DeepSpeed on the H100 GPU","publication_year":2023,"publication_date":"2023-10-28","ids":{"openalex":"https://openalex.org/W4399117356","doi":"https://doi.org/10.1145/3634769.3634806"},"language":"en","primary_location":{"id":"doi:10.1145/3634769.3634806","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3634769.3634806","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th International Green and Sustainable Computing Conference","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5010309299","display_name":"Alexander Song","orcid":"https://orcid.org/0009-0001-9195-1463"},"institutions":[{"id":"https://openalex.org/I4210149691","display_name":"William P. Clements Jr. University Hospital","ror":"https://ror.org/04qfvk625","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I4210149691"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Alexander Song","raw_affiliation_strings":["William P. Clements High School, USA"],"raw_orcid":"https://orcid.org/0009-0001-9195-1463","affiliations":[{"raw_affiliation_string":"William P. Clements High School, USA","institution_ids":["https://openalex.org/I4210149691"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015071592","display_name":"Dayuan Chen","orcid":"https://orcid.org/0009-0005-7867-6603"},"institutions":[{"id":"https://openalex.org/I13511017","display_name":"Texas State University","ror":"https://ror.org/05h9q1g27","country_code":"US","type":"education","lineage":["https://openalex.org/I13511017"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dayuan Chen","raw_affiliation_strings":["Computer Science Department, Texas State University, USA"],"raw_orcid":"https://orcid.org/0009-0005-7867-6603","affiliations":[{"raw_affiliation_string":"Computer Science Department, Texas State University, USA","institution_ids":["https://openalex.org/I13511017"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008451482","display_name":"Ziliang Zong","orcid":"https://orcid.org/0000-0003-2693-7419"},"institutions":[{"id":"https://openalex.org/I13511017","display_name":"Texas State University","ror":"https://ror.org/05h9q1g27","country_code":"US","type":"education","lineage":["https://openalex.org/I13511017"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ziliang Zong","raw_affiliation_strings":["Computer Science Department, Texas State University, USA"],"raw_orcid":"https://orcid.org/0000-0003-2693-7419","affiliations":[{"raw_affiliation_string":"Computer Science Department, Texas State University, USA","institution_ids":["https://openalex.org/I13511017"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5010309299"],"corresponding_institution_ids":["https://openalex.org/I4210149691"],"apc_list":null,"apc_paid":null,"fwci":0.471,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.66741928,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"36","last_page":"38"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.896399974822998,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.896399974822998,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11801","display_name":"Reservoir Engineering and Simulation Methods","score":0.8465999960899353,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.8210999965667725,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/carbon-footprint","display_name":"Carbon footprint","score":0.782093346118927},{"id":"https://openalex.org/keywords/footprint","display_name":"Footprint","score":0.7255510091781616},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6662344932556152},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.4542766213417053},{"id":"https://openalex.org/keywords/carbon-fibers","display_name":"Carbon fibers","score":0.42545604705810547},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.42368364334106445},{"id":"https://openalex.org/keywords/computer-graphics","display_name":"Computer graphics (images)","score":0.3326319456100464},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.32204607129096985},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.19687852263450623},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.1556587815284729},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12035468220710754},{"id":"https://openalex.org/keywords/greenhouse-gas","display_name":"Greenhouse gas","score":0.10516008734703064},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.06949928402900696}],"concepts":[{"id":"https://openalex.org/C2780936489","wikidata":"https://www.wikidata.org/wiki/Q310667","display_name":"Carbon footprint","level":3,"score":0.782093346118927},{"id":"https://openalex.org/C132943942","wikidata":"https://www.wikidata.org/wiki/Q2562511","display_name":"Footprint","level":2,"score":0.7255510091781616},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6662344932556152},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.4542766213417053},{"id":"https://openalex.org/C140205800","wikidata":"https://www.wikidata.org/wiki/Q5860","display_name":"Carbon fibers","level":3,"score":0.42545604705810547},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.42368364334106445},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.3326319456100464},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32204607129096985},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.19687852263450623},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.1556587815284729},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12035468220710754},{"id":"https://openalex.org/C47737302","wikidata":"https://www.wikidata.org/wiki/Q167336","display_name":"Greenhouse gas","level":2,"score":0.10516008734703064},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.06949928402900696},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C104779481","wikidata":"https://www.wikidata.org/wiki/Q50707","display_name":"Composite number","level":2,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3634769.3634806","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3634769.3634806","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th International Green and Sustainable Computing Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W2963809228","https://openalex.org/W3156891177","https://openalex.org/W3205849499","https://openalex.org/W3213101878","https://openalex.org/W4287204036"],"related_works":["https://openalex.org/W3104369155","https://openalex.org/W2162390224","https://openalex.org/W4389988448","https://openalex.org/W4320030328","https://openalex.org/W2031734160","https://openalex.org/W2238629651","https://openalex.org/W4366179056","https://openalex.org/W2883844485","https://openalex.org/W2897298371","https://openalex.org/W4253735668"],"abstract_inverted_index":{"The":[0,69],"advent":[1],"of":[2],"large":[3,18,80],"language":[4,19],"models":[5,20],"like":[6],"ChatGPT":[7],"and":[8,30,46,64,84,87],"GPT-4":[9],"has":[10,78],"undoubtedly":[11],"revolutionized":[12],"the":[13,35,43,60,75,89],"AI":[14],"landscape.":[15],"However,":[16],"training":[17,51,67,96],"is":[21],"a":[22,52,79],"sophisticated":[23],"process,":[24],"which":[25],"demands":[26],"substantial":[27],"computational":[28],"resources":[29],"leads":[31],"to":[32,41],"concerns":[33],"regarding":[34],"environmental":[36],"impact.":[37],"This":[38],"paper":[39],"aims":[40],"reveal":[42],"energy":[44],"usage":[45],"carbon":[47,85,90],"emissions":[48],"incurred":[49],"in":[50,94],"sizable":[53],"Open":[54],"Pretrained":[55],"Transformers":[56],"(OPT)":[57],"model":[58],"using":[59],"cutting-edge":[61],"H100":[62],"GPU":[63],"Microsoft\u2019s":[65],"DeepSpeed-Chat":[66],"framework.":[68],"experimental":[70],"results":[71],"demonstrate":[72],"that":[73],"(1)":[74],"batch":[76],"size":[77],"impact":[81],"on":[82],"accuracy":[83],"emission,":[86],"(2)":[88],"footprint":[91],"varies":[92],"greatly":[93],"each":[95],"step.":[97]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}