{"id":"https://openalex.org/W4321446145","doi":"https://doi.org/10.1145/3572848.3577487","title":"PiPAD","display_name":"PiPAD","publication_year":2023,"publication_date":"2023-02-21","ids":{"openalex":"https://openalex.org/W4321446145","doi":"https://doi.org/10.1145/3572848.3577487"},"language":"en","primary_location":{"id":"doi:10.1145/3572848.3577487","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3572848.3577487","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming","raw_type":"proceedings-article"},"type":"preprint","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/A5018599297","display_name":"Chunyang Wang","orcid":"https://orcid.org/0000-0001-7861-1061"},"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":true,"raw_author_name":"Chunyang Wang","raw_affiliation_strings":["Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102728074","display_name":"Desen Sun","orcid":"https://orcid.org/0000-0001-8630-7959"},"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":"Desen Sun","raw_affiliation_strings":["Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101593745","display_name":"Yuebin Bai","orcid":"https://orcid.org/0000-0002-2544-3989"},"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":"Yuebin Bai","raw_affiliation_strings":["Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5018599297"],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":4.0749,"has_fulltext":false,"cited_by_count":23,"citation_normalized_percentile":{"value":0.94973972,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"405","last_page":"418"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9994000196456909,"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":0.9994000196456909,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9799000024795532,"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/computer-science","display_name":"Computer science","score":0.8875167369842529},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.7574349641799927},{"id":"https://openalex.org/keywords/inefficiency","display_name":"Inefficiency","score":0.6459266543388367},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5447725653648376},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5220979452133179},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.5019731521606445},{"id":"https://openalex.org/keywords/data-transmission","display_name":"Data transmission","score":0.499345064163208},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.47142380475997925},{"id":"https://openalex.org/keywords/reuse","display_name":"Reuse","score":0.43409767746925354},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.34693336486816406},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.17437830567359924}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8875167369842529},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.7574349641799927},{"id":"https://openalex.org/C2778869765","wikidata":"https://www.wikidata.org/wiki/Q6028363","display_name":"Inefficiency","level":2,"score":0.6459266543388367},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5447725653648376},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5220979452133179},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.5019731521606445},{"id":"https://openalex.org/C557945733","wikidata":"https://www.wikidata.org/wiki/Q389772","display_name":"Data transmission","level":2,"score":0.499345064163208},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.47142380475997925},{"id":"https://openalex.org/C206588197","wikidata":"https://www.wikidata.org/wiki/Q846574","display_name":"Reuse","level":2,"score":0.43409767746925354},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.34693336486816406},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.17437830567359924},{"id":"https://openalex.org/C175444787","wikidata":"https://www.wikidata.org/wiki/Q39072","display_name":"Microeconomics","level":1,"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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3572848.3577487","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3572848.3577487","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8259535288","display_name":null,"funder_award_id":"51877004,61732002,61572062","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"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W2128853364","https://openalex.org/W2157331557","https://openalex.org/W2560674852","https://openalex.org/W2612690371","https://openalex.org/W2747329762","https://openalex.org/W2767510344","https://openalex.org/W2901504064","https://openalex.org/W2969388332","https://openalex.org/W2970929262","https://openalex.org/W2997261254","https://openalex.org/W3019863187","https://openalex.org/W3024560045","https://openalex.org/W3026076535","https://openalex.org/W3090369187","https://openalex.org/W3098625345","https://openalex.org/W3112101075","https://openalex.org/W3126367810","https://openalex.org/W3132185085","https://openalex.org/W3132695675","https://openalex.org/W3157805807","https://openalex.org/W3175110359","https://openalex.org/W3193327410","https://openalex.org/W3200735485","https://openalex.org/W3206830733","https://openalex.org/W3210361503","https://openalex.org/W3210671945","https://openalex.org/W4220703317","https://openalex.org/W4220807331","https://openalex.org/W4221164198","https://openalex.org/W4281401915","https://openalex.org/W4281778222","https://openalex.org/W4283377648","https://openalex.org/W4286902437"],"related_works":["https://openalex.org/W2058965144","https://openalex.org/W2164382479","https://openalex.org/W2146343568","https://openalex.org/W98480971","https://openalex.org/W2150291671","https://openalex.org/W2013643406","https://openalex.org/W2027972911","https://openalex.org/W2157978810","https://openalex.org/W2597809628","https://openalex.org/W1966837078"],"abstract_inverted_index":{"Dynamic":[0],"Graph":[1],"Neural":[2],"Networks":[3],"(DGNNs)":[4],"have":[5],"been":[6],"widely":[7],"applied":[8],"in":[9,111],"various":[10,131],"real-life":[11],"applications,":[12],"such":[13],"as":[14],"link":[15],"prediction":[16],"and":[17,26,35,43,54,73,88,118],"pandemic":[18],"forecast,":[19],"to":[20,102,123],"capture":[21],"both":[22,33,86],"static":[23],"structural":[24],"information":[25],"temporal":[27],"characteristics":[28],"from":[29,49,98],"dynamic":[30],"graphs.":[31],"Combining":[32],"time-dependent":[34],"-independent":[36],"components,":[37],"DGNNs":[38],"manifest":[39],"substantial":[40],"parallel":[41],"computation":[42,103],"data":[44,55,100,116],"reuse":[45],"potentials,":[46],"but":[47],"suffer":[48],"severe":[50],"memory":[51,120],"access":[52,121],"inefficiency":[53,122],"transfer":[56],"overhead":[57],"under":[58],"the":[59,79,94,99,125,141],"canonical":[60],"one-graph-at-a-time":[61],"training":[62,76,96],"pattern.":[63],"To":[64],"tackle":[65],"these":[66],"challenges,":[67],"we":[68],"propose":[69],"PiPAD,":[70],"a":[71],"Pipelined":[72],"PArallel":[74],"DGNN":[75,143],"framework":[77],"for":[78],"end-to-end":[80],"performance":[81],"optimization":[82],"on":[83,145],"GPUs.":[84],"From":[85],"algorithm":[87],"runtime":[89],"level,":[90],"PiPAD":[91,113,134],"holistically":[92],"reconstructs":[93],"overall":[95,126],"paradigm":[97],"organization":[101],"manner.":[104],"Capable":[105],"of":[106],"processing":[107],"multiple":[108],"graph":[109],"snapshots":[110],"parallel,":[112],"eliminates":[114],"unnecessary":[115],"transmission":[117],"alleviates":[119],"improve":[124],"performance.":[127],"Our":[128],"evaluation":[129],"across":[130],"datasets":[132],"shows":[133],"achieves":[135],"1.22":[136],"\u00d7":[137],"--9.57\u00d7":[138],"speedup":[139],"over":[140],"state-of-the-art":[142],"frameworks":[144],"three":[146],"representative":[147],"models.":[148]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":4}],"updated_date":"2026-03-12T08:34:05.389933","created_date":"2023-02-22T00:00:00"}
