{"id":"https://openalex.org/W7125916071","doi":"https://doi.org/10.1145/3774934.3786440","title":"ElasGNN: An Elastic Training Framework for Distributed GNN Training","display_name":"ElasGNN: An Elastic Training Framework for Distributed GNN Training","publication_year":2026,"publication_date":"2026-01-28","ids":{"openalex":"https://openalex.org/W7125916071","doi":"https://doi.org/10.1145/3774934.3786440"},"language":null,"primary_location":{"id":"doi:10.1145/3774934.3786440","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3774934.3786440","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming","raw_type":"proceedings-article"},"type":"conference-paper","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/A5101635813","display_name":"Siqi Wang","orcid":"https://orcid.org/0009-0008-0761-3287"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Siqi Wang","raw_affiliation_strings":["Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0008-0761-3287","affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124113303","display_name":"Hailong Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hailong Yang","raw_affiliation_strings":["Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-1101-7927","affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124107555","display_name":"Pengbo Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pengbo Wang","raw_affiliation_strings":["Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0001-7197-2873","affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104470067","display_name":"Hongliang Cao","orcid":"https://orcid.org/0009-0007-3051-0430"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongliang Cao","raw_affiliation_strings":["Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0007-3051-0430","affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120935885","display_name":"Yufan Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yufan Xu","raw_affiliation_strings":["Independent Researcher, Cupertino, USA"],"raw_orcid":"https://orcid.org/0000-0002-7787-6460","affiliations":[{"raw_affiliation_string":"Independent Researcher, Cupertino, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113128939","display_name":"Xuezhu Wang","orcid":"https://orcid.org/0009-0001-7293-545X"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuezhu Wang","raw_affiliation_strings":["Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0001-7293-545X","affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120891728","display_name":"Zhongzhi Luan","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongzhi Luan","raw_affiliation_strings":["Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-7186-0556","affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yi Liu","orcid":"https://orcid.org/0000-0003-1829-2817"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Liu","raw_affiliation_strings":["Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-1829-2817","affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062397627","display_name":"D. Fanyue Qian","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Depei Qian","raw_affiliation_strings":["Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-5382-1473","affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"551","last_page":"563"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.5602999925613403,"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"}},"topics":[{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.5602999925613403,"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"}},{"id":"https://openalex.org/T12292","display_name":"Graph Theory and Algorithms","score":0.2924000024795532,"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":0.029400000348687172,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.6212999820709229},{"id":"https://openalex.org/keywords/scheduling","display_name":"Scheduling (production processes)","score":0.6029999852180481},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.5475000143051147},{"id":"https://openalex.org/keywords/job-shop-scheduling","display_name":"Job shop scheduling","score":0.4713999927043915},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.46810001134872437},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.38370001316070557}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7519999742507935},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.6212999820709229},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.6029999852180481},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.5475000143051147},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5249000191688538},{"id":"https://openalex.org/C55416958","wikidata":"https://www.wikidata.org/wiki/Q6206757","display_name":"Job shop scheduling","level":3,"score":0.4713999927043915},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46810001134872437},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4198000133037567},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.38370001316070557},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.36149999499320984},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3345000147819519},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.31929999589920044},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.27709999680519104},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.27639999985694885},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.27549999952316284},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.25380000472068787}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3774934.3786440","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3774934.3786440","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","score":0.6624131202697754,"id":"https://metadata.un.org/sdg/8"}],"awards":[{"id":"https://openalex.org/G3242275911","display_name":null,"funder_award_id":"JKF-2025012343648, JKF-20240598","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G4115492246","display_name":null,"funder_award_id":"62322201, U23B2020, 62402525, U22A2028, 92373110","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W2809343047","https://openalex.org/W2914721378","https://openalex.org/W2997494090","https://openalex.org/W3096566397","https://openalex.org/W3123909522","https://openalex.org/W3157805807","https://openalex.org/W3159109662","https://openalex.org/W3159953606","https://openalex.org/W3165031368","https://openalex.org/W3172854437","https://openalex.org/W4226237846","https://openalex.org/W4283314525","https://openalex.org/W4290944486","https://openalex.org/W4318541537","https://openalex.org/W4318541676","https://openalex.org/W4321466207","https://openalex.org/W4381328668","https://openalex.org/W4384705419","https://openalex.org/W4388662057","https://openalex.org/W4388662082","https://openalex.org/W4393407062","https://openalex.org/W4394923015","https://openalex.org/W4395020669","https://openalex.org/W4409248382","https://openalex.org/W7118196029"],"related_works":[],"abstract_inverted_index":{"Graph":[0],"Neural":[1,43],"Networks":[2,44],"(GNNs)":[3],"have":[4],"emerged":[5],"as":[6],"powerful":[7],"machine":[8],"learning":[9],"models":[10],"for":[11,41,79,101,148],"numerous":[12],"graph-based":[13],"applications.":[14],"However,":[15],"existing":[16],"GNN":[17,70,80,92,153],"training":[18,23,29,37,71,87,149],"frameworks":[19],"cannot":[20,47],"scale":[21],"the":[22,55,110,127,138],"process":[24],"elastically,":[25],"resulting":[26],"in":[27],"poor":[28],"throughput":[30],"and":[31,59,95,104,130,146],"low":[32],"cluster":[33],"utilization.":[34],"Although":[35],"elastic":[36,69,86,118],"has":[38],"been":[39],"proposed":[40],"Deep":[42],"(DNNs),":[45],"it":[46],"be":[48],"directly":[49],"adopted":[50],"to":[51,54,89,107,125],"GNNs":[52],"due":[53],"prohibitive":[56],"scaling":[57,94,111],"cost":[58],"inefficient":[60],"scheduling.":[61],"In":[62],"this":[63],"paper,":[64],"we":[65],"present":[66],"ElasGNN,":[67],"an":[68,84,116],"framework":[72],"that":[73,137],"achieves":[74],"efficient":[75,85,117],"dynamic":[76],"resource":[77],"allocation":[78],"jobs.":[81],"ElasGNN":[82,114,139],"proposes":[83],"engine":[88],"achieve":[90,141],"high-performant":[91],"job":[93,143],"introduces":[96],"novel":[97],"graph":[98],"repartitioning":[99],"algorithms":[100],"both":[102],"scale-in":[103],"scale-out":[105],"processes":[106],"further":[108],"minimize":[109],"cost.":[112],"Moreover,":[113],"designs":[115],"scheduler,":[119],"utilizing":[120],"a":[121],"scaling-cost-aware":[122],"scheduling":[123],"policy":[124],"improve":[126],"GPU":[128],"utilization":[129],"system":[131],"throughput.":[132],"The":[133],"experimental":[134],"results":[135],"show":[136],"can":[140],"shorter":[142],"completion":[144],"time":[145],"makespan":[147],"jobs":[150],"of":[151],"diverse":[152],"models.":[154]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-01-29T00:00:00"}