{"id":"https://openalex.org/W4408029438","doi":"https://doi.org/10.1145/3710848.3710883","title":"Adaptive Parallel Training for Graph Neural Networks","display_name":"Adaptive Parallel Training for Graph Neural Networks","publication_year":2025,"publication_date":"2025-02-28","ids":{"openalex":"https://openalex.org/W4408029438","doi":"https://doi.org/10.1145/3710848.3710883"},"language":"en","primary_location":{"id":"doi:10.1145/3710848.3710883","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3710848.3710883","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3710848.3710883","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3710848.3710883","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5032934593","display_name":"Kaihao Ma","orcid":"https://orcid.org/0009-0001-3176-7816"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":true,"raw_author_name":"Kaihao Ma","raw_affiliation_strings":["The Chinese University of Hong Kong and AWS Shanghai AI Lab"],"raw_orcid":"https://orcid.org/0009-0001-3176-7816","affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong and AWS Shanghai AI Lab","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038657520","display_name":"Renjie Liu","orcid":"https://orcid.org/0009-0007-4332-2762"},"institutions":[{"id":"https://openalex.org/I3045169105","display_name":"Southern University of Science and Technology","ror":"https://ror.org/049tv2d57","country_code":"CN","type":"education","lineage":["https://openalex.org/I3045169105"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Renjie Liu","raw_affiliation_strings":["Southern University of Science and Technology and AWS Shanghai AI Lab"],"raw_orcid":"https://orcid.org/0009-0007-4332-2762","affiliations":[{"raw_affiliation_string":"Southern University of Science and Technology and AWS Shanghai AI Lab","institution_ids":["https://openalex.org/I3045169105"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100367774","display_name":"Xiao Yan","orcid":"https://orcid.org/0000-0002-2122-915X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao Yan","raw_affiliation_strings":["Centre for Perceptual and Interactive Intelligence and AWS Shanghai AI Lab"],"raw_orcid":"https://orcid.org/0000-0002-2122-915X","affiliations":[{"raw_affiliation_string":"Centre for Perceptual and Interactive Intelligence and AWS Shanghai AI Lab","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000220537","display_name":"Zhenkun Cai","orcid":"https://orcid.org/0000-0002-0199-4866"},"institutions":[{"id":"https://openalex.org/I4210089985","display_name":"Amazon (Germany)","ror":"https://ror.org/00b9ktm87","country_code":"DE","type":"company","lineage":["https://openalex.org/I1311688040","https://openalex.org/I4210089985"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Zhenkun Cai","raw_affiliation_strings":["Amazon"],"raw_orcid":"https://orcid.org/0000-0002-0199-4866","affiliations":[{"raw_affiliation_string":"Amazon","institution_ids":["https://openalex.org/I4210089985"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101675531","display_name":"Xiang Song","orcid":"https://orcid.org/0000-0001-5030-5054"},"institutions":[{"id":"https://openalex.org/I4210089985","display_name":"Amazon (Germany)","ror":"https://ror.org/00b9ktm87","country_code":"DE","type":"company","lineage":["https://openalex.org/I1311688040","https://openalex.org/I4210089985"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Xiang Song","raw_affiliation_strings":["Amazon"],"raw_orcid":"https://orcid.org/0000-0001-5030-5054","affiliations":[{"raw_affiliation_string":"Amazon","institution_ids":["https://openalex.org/I4210089985"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057909000","display_name":"Minjie Wang","orcid":"https://orcid.org/0009-0009-8156-1179"},"institutions":[{"id":"https://openalex.org/I4391012619","display_name":"Shanghai Artificial Intelligence Laboratory","ror":"https://ror.org/03wkvpx79","country_code":null,"type":"facility","lineage":["https://openalex.org/I4391012619"]}],"countries":[],"is_corresponding":false,"raw_author_name":"Minjie Wang","raw_affiliation_strings":["AWS Shanghai AI Lab"],"raw_orcid":"https://orcid.org/0009-0009-8156-1179","affiliations":[{"raw_affiliation_string":"AWS Shanghai AI Lab","institution_ids":["https://openalex.org/I4391012619"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100782551","display_name":"Yichao Li","orcid":"https://orcid.org/0000-0002-4657-0901"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Yichao Li","raw_affiliation_strings":["The Chinese University of Hong Kong"],"raw_orcid":"https://orcid.org/0000-0002-4657-0901","affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016082884","display_name":"James Cheng","orcid":"https://orcid.org/0000-0001-6313-6288"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"James Cheng","raw_affiliation_strings":["The Chinese University of Hong Kong"],"raw_orcid":"https://orcid.org/0000-0001-6313-6288","affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong","institution_ids":["https://openalex.org/I177725633"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5032934593"],"corresponding_institution_ids":["https://openalex.org/I177725633"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.01375657,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"29","last_page":"42"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9995999932289124,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9926000237464905,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7838109731674194},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.573383092880249},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.45573458075523376},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.43735596537590027},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43416959047317505},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3083045482635498}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7838109731674194},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.573383092880249},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.45573458075523376},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.43735596537590027},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43416959047317505},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3083045482635498},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3710848.3710883","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3710848.3710883","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3710848.3710883","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3710848.3710883","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3710848.3710883","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3710848.3710883","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4408029438.pdf"},"referenced_works_count":29,"referenced_works":["https://openalex.org/W95608104","https://openalex.org/W288065879","https://openalex.org/W569478347","https://openalex.org/W1997416175","https://openalex.org/W2053076698","https://openalex.org/W2560674852","https://openalex.org/W2807021761","https://openalex.org/W2883830791","https://openalex.org/W2969388332","https://openalex.org/W3009901425","https://openalex.org/W3012871709","https://openalex.org/W3019389960","https://openalex.org/W3086105743","https://openalex.org/W3087202534","https://openalex.org/W3096566397","https://openalex.org/W3099064659","https://openalex.org/W3100848837","https://openalex.org/W3101104221","https://openalex.org/W3158520854","https://openalex.org/W3159109662","https://openalex.org/W3159894882","https://openalex.org/W3159953606","https://openalex.org/W3206504463","https://openalex.org/W3213412677","https://openalex.org/W4220807331","https://openalex.org/W4283314525","https://openalex.org/W4321466207","https://openalex.org/W4381832157","https://openalex.org/W4387321131"],"related_works":["https://openalex.org/W230091440","https://openalex.org/W2233261550","https://openalex.org/W2810751659","https://openalex.org/W258997015","https://openalex.org/W2997094352","https://openalex.org/W3216976533","https://openalex.org/W100620283","https://openalex.org/W2495260952","https://openalex.org/W4366179611","https://openalex.org/W2996078371"],"abstract_inverted_index":{"There":[0],"are":[1],"several":[2],"strategies":[3,56,70],"to":[4,51,78,99,109,125],"parallelize":[5],"graph":[6,35],"neural":[7],"network":[8],"(GNN)":[9],"training":[10,39,59,130],"over":[11,136],"multiple":[12],"GPUs.":[13],"We":[14],"observe":[15],"that":[16,105,116],"there":[17],"is":[18,146],"no":[19],"consistent":[20],"winner":[21],"(i.e.,":[22],"with":[23,139],"the":[24,29,34,48,66,69,95,120,129],"shortest":[25],"running":[26],"time),":[27],"and":[28,41,71,83,128],"optimal":[30,121,126],"strategy":[31,85],"depends":[32],"on":[33],"dataset,":[36],"GNN":[37,58],"model,":[38],"algorithm,":[40],"hardware":[42],"configurations.":[43],"As":[44],"such,":[45],"we":[46,64,88],"design":[47,72],"APT":[49,117,145],"system":[50],"automatically":[52],"select":[53],"efficient":[54],"parallelization":[55],"for":[57],"tasks.":[60],"To":[61],"this":[62],"end,":[63],"analyze":[65],"trade-offs":[67],"of":[68,94],"simple":[73],"yet":[74],"effective":[75],"cost":[76],"models":[77],"compare":[79],"their":[80],"execution":[81,103],"time":[82,131],"facilitate":[84],"selection.":[86],"Moreover,":[87],"also":[89],"propose":[90],"a":[91,101,123,142],"general":[92],"abstraction":[93],"strategies,":[96],"which":[97],"allows":[98],"implement":[100],"unified":[102],"engine":[104],"can":[106,132],"be":[107,133],"configured":[108],"run":[110],"different":[111],"strategies.":[112],"Our":[113],"experiments":[114],"show":[115],"usually":[118],"chooses":[119],"or":[122],"close":[124],"strategy,":[127],"reduced":[134],"by":[135],"2x":[137],"compared":[138],"always":[140],"using":[141],"single":[143],"strategy.":[144],"open-source":[147],"at":[148],"https://github.com/kaihaoma/APT.":[149]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}