{"id":"https://openalex.org/W4311314339","doi":"https://doi.org/10.1109/iiswc55918.2022.00023","title":"Characterizing the Efficiency of Graph Neural Network Frameworks with a Magnifying Glass","display_name":"Characterizing the Efficiency of Graph Neural Network Frameworks with a Magnifying Glass","publication_year":2022,"publication_date":"2022-11-01","ids":{"openalex":"https://openalex.org/W4311314339","doi":"https://doi.org/10.1109/iiswc55918.2022.00023"},"language":"en","primary_location":{"id":"doi:10.1109/iiswc55918.2022.00023","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iiswc55918.2022.00023","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Symposium on Workload Characterization (IISWC)","raw_type":"proceedings-article"},"type":"article","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/A5047677335","display_name":"Xin Huang","orcid":"https://orcid.org/0000-0001-7113-5066"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xin Huang","raw_affiliation_strings":["Texas State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Texas State University","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018146250","display_name":"Jongryool Kim","orcid":"https://orcid.org/0009-0006-5938-6878"},"institutions":[{"id":"https://openalex.org/I4210112278","display_name":"SK Group (Japan)","ror":"https://ror.org/02axkyn34","country_code":"JP","type":"company","lineage":["https://openalex.org/I134353371","https://openalex.org/I4210112278"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Jongryool Kim","raw_affiliation_strings":["SK hynix America"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SK hynix America","institution_ids":["https://openalex.org/I4210112278"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087010481","display_name":"Bradley Rees","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bradley Rees","raw_affiliation_strings":["NVIDIA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NVIDIA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100622990","display_name":"Chul\u2010Ho Lee","orcid":"https://orcid.org/0000-0001-6918-976X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chul-Ho Lee","raw_affiliation_strings":["Texas State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Texas State University","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9185,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.79018603,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"160","last_page":"170"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":1.0,"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":1.0,"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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.9954000115394592,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/T12292","display_name":"Graph Theory and Algorithms","score":0.989799976348877,"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":0.6254139542579651},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.544128954410553},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4568818509578705},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4119185209274292},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.28140273690223694}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6254139542579651},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.544128954410553},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4568818509578705},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4119185209274292},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.28140273690223694}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iiswc55918.2022.00023","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iiswc55918.2022.00023","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Symposium on Workload Characterization (IISWC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8899999856948853,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W2132022337","https://openalex.org/W2533256678","https://openalex.org/W2762390662","https://openalex.org/W2765323781","https://openalex.org/W2805516822","https://openalex.org/W2916106175","https://openalex.org/W2945827377","https://openalex.org/W2961295589","https://openalex.org/W2963695795","https://openalex.org/W2963809228","https://openalex.org/W2964015378","https://openalex.org/W2964321699","https://openalex.org/W2970090796","https://openalex.org/W3034492151","https://openalex.org/W3036016037","https://openalex.org/W3080555959","https://openalex.org/W3100078588","https://openalex.org/W3101553402","https://openalex.org/W3158399077","https://openalex.org/W3159049185","https://openalex.org/W3159894882","https://openalex.org/W3167254314","https://openalex.org/W3167947860","https://openalex.org/W3181228909","https://openalex.org/W3206089854","https://openalex.org/W3206504463","https://openalex.org/W3214897310","https://openalex.org/W4206482253","https://openalex.org/W4210242600","https://openalex.org/W4224254405","https://openalex.org/W4286795917","https://openalex.org/W4287726895","https://openalex.org/W4287829537","https://openalex.org/W4288419263","https://openalex.org/W4294558607","https://openalex.org/W4295312788","https://openalex.org/W4297733535","https://openalex.org/W6679191575","https://openalex.org/W6720006811","https://openalex.org/W6726873649","https://openalex.org/W6738964360","https://openalex.org/W6745316256","https://openalex.org/W6748799445","https://openalex.org/W6751796012","https://openalex.org/W6760001035","https://openalex.org/W6760045743","https://openalex.org/W6765543928","https://openalex.org/W6766609504","https://openalex.org/W6766978945","https://openalex.org/W6774097426","https://openalex.org/W6776488958","https://openalex.org/W6779961489","https://openalex.org/W6780489652","https://openalex.org/W6781932242","https://openalex.org/W6797464607"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W4402327032","https://openalex.org/W2382290278"],"abstract_inverted_index":{"Graph":[0],"neural":[1],"networks":[2],"(GNNs)":[3],"have":[4,20,46,71],"received":[5],"great":[6],"attention":[7],"due":[8],"to":[9,150],"their":[10,33,41,92,152],"success":[11],"in":[12,65,100,154],"various":[13],"graph-related":[14],"learning":[15],"tasks.":[16],"Several":[17],"GNN":[18,30,143],"frameworks":[19,102,124,144],"then":[21],"been":[22,48,72],"developed":[23,73],"for":[24,79,182],"fast":[25],"and":[26,40,43,105,113,158,171,176,185],"easy":[27],"implementation":[28],"of":[29,82,140,156],"models.":[31],"Despite":[32],"popularity,":[34],"they":[35,88],"are":[36,58,125],"not":[37,47],"well":[38,49],"documented,":[39],"implementations":[42,99],"system":[44],"performance":[45,153],"understood.":[50],"In":[51,116,132],"particular,":[52],"unlike":[53],"the":[54,62,90,98,101,123],"traditional":[55],"GNNs":[56,70,83,149],"that":[57],"trained":[59],"based":[60],"on":[61,84,97],"entire":[63],"graph":[64,76],"a":[66,128],"full-batch":[67],"manner,":[68],"recent":[69],"with":[74,146],"different":[75,169],"sampling":[77,104],"techniques":[78],"mini-batch":[80],"training":[81,93],"large":[85],"graphs.":[86],"While":[87],"improve":[89],"scalability,":[91],"times":[94],"still":[95],"depend":[96],"as":[103],"its":[106],"associated":[107],"operations":[108],"can":[109],"introduce":[110],"non-negligible":[111],"overhead":[112],"computational":[114],"cost.":[115],"addition,":[117],"it":[118],"is":[119],"unknown":[120],"how":[121],"much":[122],"\u2018eco-friendly\u2019":[126],"from":[127],"green":[129],"computing":[130],"perspective.":[131],"this":[133],"paper,":[134],"we":[135],"provide":[136],"an":[137],"in-depth":[138],"study":[139],"two":[141],"mainstream":[142],"along":[145],"three":[147],"state-of-the-art":[148],"analyze":[151],"terms":[155],"runtime":[157],"power/energy":[159],"consumption.":[160],"We":[161],"conduct":[162],"extensive":[163],"bench":[164],"mark":[165],"experiments":[166],"at":[167],"several":[168],"levels":[170],"present":[172],"detailed":[173],"analysis":[174],"results":[175],"observations,":[177],"which":[178],"could":[179],"be":[180],"helpful":[181],"further":[183],"improvement":[184],"optimization.":[186]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
