{"id":"https://openalex.org/W4308090237","doi":"https://doi.org/10.1109/hpec55821.2022.9926375","title":"Benchmarking Resource Usage for Efficient Distributed Deep Learning","display_name":"Benchmarking Resource Usage for Efficient Distributed Deep Learning","publication_year":2022,"publication_date":"2022-09-19","ids":{"openalex":"https://openalex.org/W4308090237","doi":"https://doi.org/10.1109/hpec55821.2022.9926375"},"language":"en","primary_location":{"id":"doi:10.1109/hpec55821.2022.9926375","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hpec55821.2022.9926375","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE High Performance Extreme Computing Conference (HPEC)","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/A5090160831","display_name":"Nathan C. Frey","orcid":"https://orcid.org/0000-0001-5291-6131"},"institutions":[{"id":"https://openalex.org/I4210122954","display_name":"MIT Lincoln Laboratory","ror":"https://ror.org/022z6jk58","country_code":"US","type":"facility","lineage":["https://openalex.org/I4210122954","https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Nathan C. Frey","raw_affiliation_strings":["MIT Lincoln Laboratory"],"affiliations":[{"raw_affiliation_string":"MIT Lincoln Laboratory","institution_ids":["https://openalex.org/I4210122954"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100733204","display_name":"Baolin Li","orcid":"https://orcid.org/0000-0001-9778-1023"},"institutions":[{"id":"https://openalex.org/I87182695","display_name":"Universidad del Noreste","ror":"https://ror.org/02ahky613","country_code":"MX","type":"education","lineage":["https://openalex.org/I87182695"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"Baolin Li","raw_affiliation_strings":["Northeastern University"],"affiliations":[{"raw_affiliation_string":"Northeastern University","institution_ids":["https://openalex.org/I87182695"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004090802","display_name":"Joseph McDonald","orcid":"https://orcid.org/0009-0004-6477-8476"},"institutions":[{"id":"https://openalex.org/I4210122954","display_name":"MIT Lincoln Laboratory","ror":"https://ror.org/022z6jk58","country_code":"US","type":"facility","lineage":["https://openalex.org/I4210122954","https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Joseph McDonald","raw_affiliation_strings":["MIT Lincoln Laboratory"],"affiliations":[{"raw_affiliation_string":"MIT Lincoln Laboratory","institution_ids":["https://openalex.org/I4210122954"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045617693","display_name":"Dan Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I4210122954","display_name":"MIT Lincoln Laboratory","ror":"https://ror.org/022z6jk58","country_code":"US","type":"facility","lineage":["https://openalex.org/I4210122954","https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dan Zhao","raw_affiliation_strings":["MIT Lincoln Laboratory"],"affiliations":[{"raw_affiliation_string":"MIT Lincoln Laboratory","institution_ids":["https://openalex.org/I4210122954"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064275902","display_name":"Michael Jones","orcid":"https://orcid.org/0000-0001-5215-2346"},"institutions":[{"id":"https://openalex.org/I4210122954","display_name":"MIT Lincoln Laboratory","ror":"https://ror.org/022z6jk58","country_code":"US","type":"facility","lineage":["https://openalex.org/I4210122954","https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Michael Jones","raw_affiliation_strings":["MIT Lincoln Laboratory"],"affiliations":[{"raw_affiliation_string":"MIT Lincoln Laboratory","institution_ids":["https://openalex.org/I4210122954"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072368385","display_name":"David Bestor","orcid":"https://orcid.org/0009-0002-7684-1191"},"institutions":[{"id":"https://openalex.org/I4210122954","display_name":"MIT Lincoln Laboratory","ror":"https://ror.org/022z6jk58","country_code":"US","type":"facility","lineage":["https://openalex.org/I4210122954","https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David Bestor","raw_affiliation_strings":["MIT Lincoln Laboratory"],"affiliations":[{"raw_affiliation_string":"MIT Lincoln Laboratory","institution_ids":["https://openalex.org/I4210122954"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074406596","display_name":"Devesh Tiwari","orcid":"https://orcid.org/0000-0002-7253-2458"},"institutions":[{"id":"https://openalex.org/I87182695","display_name":"Universidad del Noreste","ror":"https://ror.org/02ahky613","country_code":"MX","type":"education","lineage":["https://openalex.org/I87182695"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"Devesh Tiwari","raw_affiliation_strings":["Northeastern University"],"affiliations":[{"raw_affiliation_string":"Northeastern University","institution_ids":["https://openalex.org/I87182695"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043450560","display_name":"Vijay Gadepally","orcid":"https://orcid.org/0000-0002-4598-2808"},"institutions":[{"id":"https://openalex.org/I4210122954","display_name":"MIT Lincoln Laboratory","ror":"https://ror.org/022z6jk58","country_code":"US","type":"facility","lineage":["https://openalex.org/I4210122954","https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vijay Gadepally","raw_affiliation_strings":["MIT Lincoln Laboratory"],"affiliations":[{"raw_affiliation_string":"MIT Lincoln Laboratory","institution_ids":["https://openalex.org/I4210122954"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103227438","display_name":"Siddharth Samsi","orcid":"https://orcid.org/0009-0000-2884-9688"},"institutions":[{"id":"https://openalex.org/I4210122954","display_name":"MIT Lincoln Laboratory","ror":"https://ror.org/022z6jk58","country_code":"US","type":"facility","lineage":["https://openalex.org/I4210122954","https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Siddharth Samsi","raw_affiliation_strings":["MIT Lincoln Laboratory"],"affiliations":[{"raw_affiliation_string":"MIT Lincoln Laboratory","institution_ids":["https://openalex.org/I4210122954"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":9,"corresponding_author_ids":["https://openalex.org/A5090160831"],"corresponding_institution_ids":["https://openalex.org/I4210122954"],"apc_list":null,"apc_paid":null,"fwci":1.1073,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.78808774,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9991999864578247,"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":0.9991999864578247,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.998199999332428,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9952999949455261,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7953507900238037},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7070841789245605},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7025260925292969},{"id":"https://openalex.org/keywords/workflow","display_name":"Workflow","score":0.7013803720474243},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.6565873026847839},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5006101131439209},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.49289989471435547},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.4763070344924927},{"id":"https://openalex.org/keywords/resource","display_name":"Resource (disambiguation)","score":0.47487518191337585},{"id":"https://openalex.org/keywords/resource-allocation","display_name":"Resource allocation","score":0.4669227600097656},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.4541248679161072},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.44153285026550293},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4316745102405548},{"id":"https://openalex.org/keywords/graphics","display_name":"Graphics","score":0.42598438262939453},{"id":"https://openalex.org/keywords/power-budget","display_name":"Power budget","score":0.42421025037765503},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3961726725101471},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.20579811930656433},{"id":"https://openalex.org/keywords/electric-power-system","display_name":"Electric power system","score":0.14198237657546997},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.12149804830551147}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7953507900238037},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7070841789245605},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7025260925292969},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.7013803720474243},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.6565873026847839},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5006101131439209},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.49289989471435547},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4763070344924927},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.47487518191337585},{"id":"https://openalex.org/C29202148","wikidata":"https://www.wikidata.org/wiki/Q287260","display_name":"Resource allocation","level":2,"score":0.4669227600097656},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.4541248679161072},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.44153285026550293},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4316745102405548},{"id":"https://openalex.org/C21442007","wikidata":"https://www.wikidata.org/wiki/Q1027879","display_name":"Graphics","level":2,"score":0.42598438262939453},{"id":"https://openalex.org/C149768029","wikidata":"https://www.wikidata.org/wiki/Q1509342","display_name":"Power budget","level":4,"score":0.42421025037765503},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3961726725101471},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.20579811930656433},{"id":"https://openalex.org/C89227174","wikidata":"https://www.wikidata.org/wiki/Q2388981","display_name":"Electric power system","level":3,"score":0.14198237657546997},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.12149804830551147},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/hpec55821.2022.9926375","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hpec55821.2022.9926375","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE High Performance Extreme Computing Conference (HPEC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.550000011920929}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":54,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1686810756","https://openalex.org/W2080635178","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2290847742","https://openalex.org/W2515080096","https://openalex.org/W2553303224","https://openalex.org/W2558748708","https://openalex.org/W2734941459","https://openalex.org/W2747329762","https://openalex.org/W2750779823","https://openalex.org/W2798544842","https://openalex.org/W2809090039","https://openalex.org/W2883672905","https://openalex.org/W2903557836","https://openalex.org/W2955425717","https://openalex.org/W2969580338","https://openalex.org/W2997878467","https://openalex.org/W3013567118","https://openalex.org/W3016339201","https://openalex.org/W3034429256","https://openalex.org/W3037032032","https://openalex.org/W3037585619","https://openalex.org/W3042964270","https://openalex.org/W3081659545","https://openalex.org/W3082020764","https://openalex.org/W3095809712","https://openalex.org/W3100157108","https://openalex.org/W3105753409","https://openalex.org/W3106525532","https://openalex.org/W3117961507","https://openalex.org/W3133702157","https://openalex.org/W3138994021","https://openalex.org/W3157286395","https://openalex.org/W3197816522","https://openalex.org/W4205140476","https://openalex.org/W4287204036","https://openalex.org/W4287586570","https://openalex.org/W4287828570","https://openalex.org/W4288090629","https://openalex.org/W4288796004","https://openalex.org/W4289276774","https://openalex.org/W4298422451","https://openalex.org/W4300537282","https://openalex.org/W6637373629","https://openalex.org/W6744307745","https://openalex.org/W6752495264","https://openalex.org/W6755207826","https://openalex.org/W6758283263","https://openalex.org/W6760045743","https://openalex.org/W6769540564","https://openalex.org/W6782268329","https://openalex.org/W6810461958"],"related_works":["https://openalex.org/W4238897586","https://openalex.org/W435179959","https://openalex.org/W2619091065","https://openalex.org/W2059640416","https://openalex.org/W1490753184","https://openalex.org/W2284465472","https://openalex.org/W2291782699","https://openalex.org/W1993948687","https://openalex.org/W2011676020","https://openalex.org/W2000169967"],"abstract_inverted_index":{"Deep":[0],"learning":[1,162],"(DL)":[2],"workflows":[3],"demand":[4],"an":[5,56],"ever-increasing":[6],"budget":[7],"of":[8,58],"compute":[9,35,83,132],"and":[10,31,36,45,68,86,94,101,106,116,134,145],"energy":[11,37,87,135,157],"in":[12,150],"order":[13],"to":[14,23,71,99],"achieve":[15],"outsized":[16],"gains.":[17],"As":[18],"such,":[19],"it":[20],"becomes":[21],"essential":[22],"understand":[24],"how":[25,126],"different":[26,43,104,160],"deep":[27,59,161],"neural":[28],"networks":[29,60],"(DNNs)":[30],"training":[32,55,127],"leverage":[33],"increasing":[34],"resources-especially":[38],"specialized":[39],"computationally-intensive":[40],"models":[41,123],"across":[42],"domains":[44],"applications.":[46],"In":[47],"this":[48],"paper,":[49],"we":[50],"conduct":[51],"over":[52],"3,400":[53],"experiments":[54,80],"array":[57],"representing":[61],"various":[62,114],"domains/tasks-natural":[63],"language":[64],"processing,":[65],"computer":[66],"vision,":[67],"chemistry-on":[69],"up":[70],"424":[72],"graphics":[73],"processing":[74],"units":[75],"(GPUs).":[76],"During":[77],"training,":[78],"our":[79],"systematically":[81],"vary":[82],"resource":[84,115,152],"characteristics":[85],"-saving":[88],"mechanisms":[89],"such":[90],"as":[91],"power":[92,121],"utilization":[93],"GPU":[95],"clock":[96],"rate":[97],"limits":[98],"capture":[100],"illustrate":[102],"the":[103],"trade-offs":[105],"scaling":[107],"behaviors":[108],"each":[109],"representative":[110],"model":[111],"exhibits":[112],"under":[113],"energy-constrained":[117],"regimes.":[118],"We":[119,137],"fit":[120],"law":[122],"that":[124,139],"describe":[125],"time":[128],"scales":[129],"with":[130,164],"available":[131],"resources":[133],"constraints.":[136],"anticipate":[138],"these":[140],"findings":[141],"will":[142],"help":[143],"inform":[144],"guide":[146],"high-performance":[147],"computing":[148],"providers":[149],"optimizing":[151],"utilization,":[153],"by":[154],"selectively":[155],"reducing":[156],"consumption":[158],"for":[159],"tasks/workflows":[163],"minimal":[165],"impact":[166],"on":[167],"training.":[168]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
