{"id":"https://openalex.org/W7155089811","doi":"https://doi.org/10.48550/arxiv.2604.16715","title":"Scalable and Adaptive Parallel Training of Graph Transformer on Large Graphs","display_name":"Scalable and Adaptive Parallel Training of Graph Transformer on Large Graphs","publication_year":2026,"publication_date":"2026-04-17","ids":{"openalex":"https://openalex.org/W7155089811","doi":"https://doi.org/10.48550/arxiv.2604.16715"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.16715","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.16715","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.16715","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5002465188","display_name":"Jun-Liang Lin","orcid":"https://orcid.org/0000-0001-5105-7574"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Jun-Liang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134132093","display_name":"Kamesh Madduri","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Madduri, Kamesh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134174517","display_name":"Mahmut Taylan Kandemir","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kandemir, Mahmut Taylan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.8270000219345093,"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":0.8270000219345093,"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":0.12290000170469284,"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":0.007199999876320362,"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/scalability","display_name":"Scalability","score":0.6686000227928162},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5249000191688538},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.490200012922287},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.39489999413490295},{"id":"https://openalex.org/keywords/graph-bandwidth","display_name":"Graph bandwidth","score":0.35830000042915344},{"id":"https://openalex.org/keywords/implementation","display_name":"Implementation","score":0.335099995136261},{"id":"https://openalex.org/keywords/graph-algorithms","display_name":"Graph algorithms","score":0.328000009059906},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.328000009059906}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8269000053405762},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6686000227928162},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5249000191688538},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5202000141143799},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.4952999949455261},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.490200012922287},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.39489999413490295},{"id":"https://openalex.org/C134727501","wikidata":"https://www.wikidata.org/wiki/Q5597073","display_name":"Graph bandwidth","level":5,"score":0.35830000042915344},{"id":"https://openalex.org/C26713055","wikidata":"https://www.wikidata.org/wiki/Q245962","display_name":"Implementation","level":2,"score":0.335099995136261},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.328000009059906},{"id":"https://openalex.org/C2986651925","wikidata":"https://www.wikidata.org/wiki/Q1514868","display_name":"Graph algorithms","level":3,"score":0.328000009059906},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.3215999901294708},{"id":"https://openalex.org/C48903430","wikidata":"https://www.wikidata.org/wiki/Q491370","display_name":"Graph partition","level":3,"score":0.30970001220703125},{"id":"https://openalex.org/C64339825","wikidata":"https://www.wikidata.org/wiki/Q722659","display_name":"Graph property","level":5,"score":0.28859999775886536},{"id":"https://openalex.org/C97042676","wikidata":"https://www.wikidata.org/wiki/Q5597097","display_name":"Graph reduction","level":3,"score":0.2854999899864197},{"id":"https://openalex.org/C17169500","wikidata":"https://www.wikidata.org/wiki/Q3033506","display_name":"Null graph","level":5,"score":0.28519999980926514},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.2822999954223633},{"id":"https://openalex.org/C13251829","wikidata":"https://www.wikidata.org/wiki/Q3085841","display_name":"Dense graph","level":5,"score":0.26910001039505005},{"id":"https://openalex.org/C558772884","wikidata":"https://www.wikidata.org/wiki/Q1508564","display_name":"Graph rewriting","level":3,"score":0.2646999955177307},{"id":"https://openalex.org/C5737132","wikidata":"https://www.wikidata.org/wiki/Q1101814","display_name":"Clique-width","level":5,"score":0.26350000500679016}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.16715","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.16715","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.16715","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.16715","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.4529525637626648}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Graph":[0],"foundation":[1,164],"models":[2],"have":[3],"demonstrated":[4],"remarkable":[5],"adaptability":[6],"across":[7],"diverse":[8],"downstream":[9],"tasks":[10],"through":[11],"large-scale":[12],"pretraining":[13],"on":[14,39,57,91],"graphs.":[15,41],"However,":[16],"existing":[17],"implementations":[18],"of":[19,101,153],"the":[20,48,59,92,147,151],"backbone":[21],"model,":[22],"graph":[23,44,50,60,81,93,108,126,154,163],"transformers,":[24,82,155],"are":[25],"typically":[26],"limited":[27],"to":[28,32,112,121,133,140,159],"single-GPU":[29],"systems,":[30],"leading":[31],"long":[33],"training":[34,46,78],"times":[35],"or":[36],"out-of-memory":[37],"issues":[38],"large":[40,125],"Moreover,":[42],"parallelizing":[43],"transformer":[45],"over":[47],"full":[49],"is":[51],"challenging,":[52],"as":[53,66,161],"efficiency":[54],"depends":[55],"heavily":[56],"both":[58],"structure":[61,94],"and":[62,68,86,95,114],"system":[63,137],"characteristics,":[64],"such":[65],"bandwidth":[67],"memory":[69,116],"capacity.":[70],"In":[71],"this":[72],"work,":[73],"we":[74,105],"introduce":[75],"a":[76],"distributed":[77,102],"framework":[79,130,149],"for":[80],"which":[83],"automatically":[84],"selects":[85],"optimizes":[87],"parallelization":[88],"strategies":[89],"based":[90],"hardware":[96],"configuration.":[97],"With":[98],"our":[99,128],"implementation":[100],"sparse":[103,107],"operations,":[104],"accelerate":[106],"attention":[109],"by":[110,118],"up":[111,132,139],"3.8x":[113],"reduce":[115],"consumption":[117],"78%":[119],"compared":[120],"state-of-the-art":[122],"frameworks.":[123],"On":[124],"benchmarks,":[127],"proposed":[129,148],"achieves":[131],"6x":[134],"speedup":[135],"with":[136],"scaling":[138],"8":[141],"GPUs.":[142],"These":[143],"results":[144],"demonstrate":[145],"that":[146],"improves":[150],"scalability":[152],"bringing":[156],"them":[157],"closer":[158],"serving":[160],"practical":[162],"models.":[165]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-22T00:00:00"}