{"id":"https://openalex.org/W4399164756","doi":"https://doi.org/10.1145/3662010.3663443","title":"In situ neighborhood sampling for large-scale GNN training","display_name":"In situ neighborhood sampling for large-scale GNN training","publication_year":2024,"publication_date":"2024-05-30","ids":{"openalex":"https://openalex.org/W4399164756","doi":"https://doi.org/10.1145/3662010.3663443"},"language":"en","primary_location":{"id":"doi:10.1145/3662010.3663443","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3662010.3663443","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 20th International Workshop on Data Management on New Hardware","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3662010.3663443","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5098942347","display_name":"Yuhang Song","orcid":"https://orcid.org/0009-0004-2006-3137"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yuhang Song","raw_affiliation_strings":["Boston University"],"raw_orcid":"https://orcid.org/0009-0004-2006-3137","affiliations":[{"raw_affiliation_string":"Boston University","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079121374","display_name":"Po Hao Chen","orcid":"https://orcid.org/0009-0005-8164-7061"},"institutions":[{"id":"https://openalex.org/I175594653","display_name":"John Brown University","ror":"https://ror.org/02ct41q97","country_code":"US","type":"education","lineage":["https://openalex.org/I175594653"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Po Hao Chen","raw_affiliation_strings":["Brown University"],"raw_orcid":"https://orcid.org/0009-0005-8164-7061","affiliations":[{"raw_affiliation_string":"Brown University","institution_ids":["https://openalex.org/I175594653"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yuchen Lu","orcid":"https://orcid.org/0009-0003-3027-6462"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuchen Lu","raw_affiliation_strings":["Boston University"],"raw_orcid":"https://orcid.org/0009-0003-3027-6462","affiliations":[{"raw_affiliation_string":"Boston University","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5098942349","display_name":"Naima Abrar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Naima Abrar","raw_affiliation_strings":["Boston University"],"raw_orcid":"https://orcid.org/0009-0000-3591-2120","affiliations":[{"raw_affiliation_string":"Boston University","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068567119","display_name":"Vasiliki Kalavri","orcid":"https://orcid.org/0000-0001-8219-4862"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vasiliki Kalavri","raw_affiliation_strings":["Boston University"],"raw_orcid":"https://orcid.org/0000-0001-8219-4862","affiliations":[{"raw_affiliation_string":"Boston University","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5098942347"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.3245,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.82967399,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9940999746322632,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.9940999746322632,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9886000156402588,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.988099992275238,"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":0.6419183611869812},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.560937225818634},{"id":"https://openalex.org/keywords/in-situ","display_name":"In situ","score":0.5163811445236206},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4806060492992401},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.46340489387512207},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4289375841617584},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3272044062614441},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.26956871151924133},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.11542406678199768},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.10083726048469543}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6419183611869812},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.560937225818634},{"id":"https://openalex.org/C2777822432","wikidata":"https://www.wikidata.org/wiki/Q216681","display_name":"In situ","level":2,"score":0.5163811445236206},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4806060492992401},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.46340489387512207},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4289375841617584},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3272044062614441},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.26956871151924133},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.11542406678199768},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.10083726048469543},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3662010.3663443","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3662010.3663443","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 20th International Workshop on Data Management on New Hardware","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3662010.3663443","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3662010.3663443","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 20th International Workshop on Data Management on New Hardware","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.46000000834465027,"id":"https://metadata.un.org/sdg/13","display_name":"Climate action"}],"awards":[{"id":"https://openalex.org/G1108475222","display_name":null,"funder_award_id":"#2237193","funder_id":"https://openalex.org/F4320323817","funder_display_name":"Universitas Brawijaya"}],"funders":[{"id":"https://openalex.org/F4320323817","display_name":"Universitas Brawijaya","ror":"https://ror.org/01wk3d929"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1540606052","https://openalex.org/W2017513064","https://openalex.org/W2021890516","https://openalex.org/W2177227417","https://openalex.org/W2296407087","https://openalex.org/W2340222647","https://openalex.org/W2624431344","https://openalex.org/W2774217687","https://openalex.org/W2788919350","https://openalex.org/W2809418595","https://openalex.org/W2890703109","https://openalex.org/W2963066159","https://openalex.org/W3010555542","https://openalex.org/W3043776758","https://openalex.org/W3096566397","https://openalex.org/W3100848837","https://openalex.org/W3157805807","https://openalex.org/W3158520854","https://openalex.org/W3159894882","https://openalex.org/W4212774754","https://openalex.org/W4220807331","https://openalex.org/W4234493895","https://openalex.org/W4281725510","https://openalex.org/W4290944486","https://openalex.org/W4320858068","https://openalex.org/W4372267520","https://openalex.org/W4383816138","https://openalex.org/W4387321131","https://openalex.org/W6796909790"],"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/W2090412404"],"abstract_inverted_index":{"Graph":[0],"Neural":[1],"Network":[2],"(GNN)":[3],"training":[4],"algorithms":[5],"commonly":[6],"perform":[7],"neighborhood":[8],"sampling":[9,24,103,117,126],"to":[10,33,54,58,81,113,123],"construct":[11],"fixed-size":[12],"mini-batches":[13],"for":[14,35],"weight":[15],"aggregation":[16],"on":[17,25],"GPUs.":[18],"State-of-the-art":[19],"disk-based":[20],"GNN":[21,85],"frameworks":[22],"compute":[23,79],"the":[26,68],"CPU,":[27],"transferring":[28],"edge":[29],"partitions":[30],"from":[31],"disk":[32],"memory":[34,56],"every":[36],"mini-batch.":[37],"We":[38,87],"argue":[39],"that":[40,76,106],"this":[41,64],"design":[42],"incurs":[43],"significant":[44],"waste":[45],"of":[46],"PCIe":[47],"bandwidth,":[48],"as":[49],"entire":[50],"neighborhoods":[51],"are":[52],"transferred":[53],"main":[55],"only":[57],"be":[59],"discarded":[60],"after":[61],"sampling.":[62],"In":[63],"paper,":[65],"we":[66],"make":[67],"first":[69],"step":[70],"towards":[71],"an":[72],"inherently":[73],"different":[74],"approach":[75],"harnesses":[77],"near-storage":[78],"technology":[80],"achieve":[82],"efficient":[83],"large-scale":[84],"training.":[86],"target":[88],"a":[89,100,114],"single":[90],"machine":[91],"with":[92],"one":[93],"or":[94],"more":[95],"SmartSSD":[96],"devices":[97],"and":[98],"develop":[99],"high-throughput,":[101],"epoch-wide":[102],"FPGA":[104],"kernel":[105],"enables":[107],"pipelining":[108],"across":[109],"epochs.":[110],"When":[111],"compared":[112],"baseline":[115],"random-access":[116],"kernel,":[118],"our":[119],"solution":[120],"achieves":[121],"up":[122],"4.26\u00d7":[124],"lower":[125],"time":[127],"per":[128],"epoch.":[129]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
