{"id":"https://openalex.org/W4416024779","doi":"https://doi.org/10.1109/icc59461.2026.11588116","title":"ScaleDL: Towards Scalable and Efficient Runtime Prediction for Distributed Deep Learning Workloads","display_name":"ScaleDL: Towards Scalable and Efficient Runtime Prediction for Distributed Deep Learning Workloads","publication_year":2026,"publication_date":"2026-05-24","ids":{"openalex":"https://openalex.org/W4416024779","doi":"https://doi.org/10.1109/icc59461.2026.11588116"},"language":null,"primary_location":{"id":"doi:10.1109/icc59461.2026.11588116","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc59461.2026.11588116","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2026 - IEEE International Conference on Communications","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2511.04162","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100666814","display_name":"Xiaokai Wang","orcid":"https://orcid.org/0000-0001-5396-985X"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaokai Wang","raw_affiliation_strings":["Tianjin University,College of Intelligence and Computing,Tianjin,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tianjin University,College of Intelligence and Computing,Tianjin,China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000210993","display_name":"Shaoyuan Huang","orcid":"https://orcid.org/0000-0002-4091-6457"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shaoyuan Huang","raw_affiliation_strings":["Tianjin University,College of Intelligence and Computing,Tianjin,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tianjin University,College of Intelligence and Computing,Tianjin,China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100377773","display_name":"Yuting Li","orcid":"https://orcid.org/0000-0003-0077-1684"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuting Li","raw_affiliation_strings":["Tianjin University,College of Intelligence and Computing,Tianjin,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tianjin University,College of Intelligence and Computing,Tianjin,China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100363480","display_name":"Xiaofei Wang","orcid":"https://orcid.org/0000-0003-0716-3243"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaofei Wang","raw_affiliation_strings":["Tianjin University,College of Intelligence and Computing,Tianjin,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tianjin University,College of Intelligence and Computing,Tianjin,China","institution_ids":["https://openalex.org/I162868743"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I162868743"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.01368409,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.588100016117096,"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":0.588100016117096,"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/T14347","display_name":"Big Data and Digital Economy","score":0.05270000174641609,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.0340999998152256,"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/scalability","display_name":"Scalability","score":0.6984999775886536},{"id":"https://openalex.org/keywords/generalizability-theory","display_name":"Generalizability theory","score":0.6262999773025513},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5812000036239624},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5740000009536743},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.557699978351593},{"id":"https://openalex.org/keywords/data-collection","display_name":"Data collection","score":0.4875999987125397},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.3628000020980835},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.3619000017642975},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.352400004863739}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8400999903678894},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6984999775886536},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.6262999773025513},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5812000036239624},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5740000009536743},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.557699978351593},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5360999703407288},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5349000096321106},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.4875999987125397},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.446399986743927},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37529999017715454},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3628000020980835},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.3619000017642975},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.352400004863739},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.3474999964237213},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.3287999927997589},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.3276999890804291},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.3244999945163727},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.3228999972343445},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.3163999915122986},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.3133000135421753},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.30660000443458557},{"id":"https://openalex.org/C2777115002","wikidata":"https://www.wikidata.org/wiki/Q7168246","display_name":"Performance prediction","level":2,"score":0.3012999892234802},{"id":"https://openalex.org/C489000","wikidata":"https://www.wikidata.org/wiki/Q747385","display_name":"Data flow diagram","level":2,"score":0.289900004863739},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.27000001072883606},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2696000039577484},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.26589998602867126},{"id":"https://openalex.org/C96324660","wikidata":"https://www.wikidata.org/wiki/Q205446","display_name":"Dataflow","level":2,"score":0.2644999921321869},{"id":"https://openalex.org/C153740404","wikidata":"https://www.wikidata.org/wiki/Q671224","display_name":"Data center","level":2,"score":0.26269999146461487}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/icc59461.2026.11588116","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc59461.2026.11588116","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2026 - IEEE International Conference on Communications","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2511.04162","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.04162","pdf_url":"https://arxiv.org/pdf/2511.04162","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2511.04162","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2511.04162","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2511.04162","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.04162","pdf_url":"https://arxiv.org/pdf/2511.04162","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416024779.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Deep":[0],"neural":[1,121],"networks":[2],"(DNNs)":[3],"form":[4],"the":[5,142,152],"cornerstone":[6],"of":[7,15,154],"modern":[8],"AI":[9],"services,":[10],"supporting":[11],"a":[12,85,89,93,109],"wide":[13],"range":[14],"applications,":[16],"including":[17],"autonomous":[18],"driving,":[19],"chatbots,":[20],"and":[21,29,36,54,68,98,132,166,173],"recommendation":[22],"systems.":[23],"As":[24],"models":[25,158],"increase":[26],"in":[27],"size":[28],"complexity,":[30],"DNN":[31,129,157],"workloads":[32,153],"such":[33],"as":[34],"training":[35],"inference":[37],"tasks":[38],"impose":[39],"unprecedented":[40],"demands":[41],"on":[42,60,151],"distributed":[43],"computing":[44],"resources,":[45],"making":[46],"accurate":[47,128],"runtime":[48,111,130,163],"prediction":[49,112,131,164],"essential":[50],"for":[51,88],"optimizing":[52],"development":[53],"resource":[55],"allocation.":[56],"Traditional":[57],"methods":[58],"rely":[59],"additive":[61],"computational":[62],"unit":[63],"models,":[64],"limiting":[65],"their":[66],"accuracy":[67,165],"generalizability.":[69],"In":[70],"contrast,":[71],"graph-enhanced":[72],"modeling":[73,118],"improves":[74],"performance":[75],"but":[76],"significantly":[77],"increases":[78],"data":[79,99,147],"collection":[80,100,148],"costs.":[81,101,149],"Therefore,":[82],"there":[83],"is":[84],"critical":[86],"need":[87],"method":[90,144],"that":[91,114,160],"strikes":[92],"balance":[94],"between":[95],"accuracy,":[96],"generalizability,":[97,167],"To":[102],"address":[103],"these":[104],"challenges,":[105],"we":[106,140],"propose":[107],"ScaleDL,":[108],"novel":[110],"framework":[113],"combines":[115],"nonlinear":[116],"layer-wise":[117],"with":[119],"graph":[120],"network":[122,137],"(GNN)-based":[123],"cross-layer":[124],"interaction":[125],"mechanism,":[126],"enabling":[127],"hierarchical":[133],"generalizability":[134],"across":[135],"different":[136],"architectures.":[138],"Additionally,":[139],"employ":[141],"D-optimal":[143],"to":[145,179],"reduce":[146],"Experiments":[150],"five":[155],"popular":[156],"demonstrate":[159],"ScaleDL":[161],"enhances":[162],"achieving":[168],"6":[169],"times":[170,175],"lower":[171,176],"MRE":[172],"5":[174],"RMSE":[177],"compared":[178],"baseline":[180],"models.":[181]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-11-08T00:00:00"}