{"id":"https://openalex.org/W2906007643","doi":"https://doi.org/10.1145/3302424.3303957","title":"Parallax","display_name":"Parallax","publication_year":2019,"publication_date":"2019-03-22","ids":{"openalex":"https://openalex.org/W2906007643","doi":"https://doi.org/10.1145/3302424.3303957","mag":"2906007643"},"language":"en","primary_location":{"id":"doi:10.1145/3302424.3303957","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3302424.3303957","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Fourteenth EuroSys Conference 2019","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/A5101527137","display_name":"Soojeong Kim","orcid":"https://orcid.org/0000-0002-8849-6420"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Soojeong Kim","raw_affiliation_strings":["Seoul National University"],"affiliations":[{"raw_affiliation_string":"Seoul National University","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016336185","display_name":"Gyeong-In Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Gyeong-In Yu","raw_affiliation_strings":["Seoul National University"],"affiliations":[{"raw_affiliation_string":"Seoul National University","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061810331","display_name":"Hojin Park","orcid":null},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hojin Park","raw_affiliation_strings":["Seoul National University"],"affiliations":[{"raw_affiliation_string":"Seoul National University","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101543585","display_name":"Sung\u2010Woo Cho","orcid":"https://orcid.org/0000-0003-3786-6484"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sungwoo Cho","raw_affiliation_strings":["Seoul National University"],"affiliations":[{"raw_affiliation_string":"Seoul National University","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089607402","display_name":"Eunji Jeong","orcid":null},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Eunji Jeong","raw_affiliation_strings":["Seoul National University"],"affiliations":[{"raw_affiliation_string":"Seoul National University","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072880323","display_name":"Hyeonmin Ha","orcid":null},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hyeonmin Ha","raw_affiliation_strings":["Seoul National University"],"affiliations":[{"raw_affiliation_string":"Seoul National University","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101421510","display_name":"Sanha Lee","orcid":"https://orcid.org/0000-0002-1125-9814"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sanha Lee","raw_affiliation_strings":["Seoul National University"],"affiliations":[{"raw_affiliation_string":"Seoul National University","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081591481","display_name":"Joo Seong Jeong","orcid":null},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Joo Seong Jeong","raw_affiliation_strings":["Seoul National University"],"affiliations":[{"raw_affiliation_string":"Seoul National University","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083084972","display_name":"Byung-Gon Chun","orcid":"https://orcid.org/0000-0002-9863-7186"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Byung-Gon Chun","raw_affiliation_strings":["Seoul National University"],"affiliations":[{"raw_affiliation_string":"Seoul National University","institution_ids":["https://openalex.org/I139264467"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":9,"corresponding_author_ids":["https://openalex.org/A5101527137"],"corresponding_institution_ids":["https://openalex.org/I139264467"],"apc_list":null,"apc_paid":null,"fwci":4.4952,"has_fulltext":false,"cited_by_count":66,"citation_normalized_percentile":{"value":0.95676025,"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":"1","last_page":"15"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9993000030517578,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9987000226974487,"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":0.8229069709777832},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.8160606622695923},{"id":"https://openalex.org/keywords/parallax","display_name":"Parallax","score":0.7532254457473755},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6617966890335083},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.655454695224762},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5919144749641418},{"id":"https://openalex.org/keywords/server","display_name":"Server","score":0.5334759950637817},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.46724677085876465},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43450090289115906},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.41868507862091064},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41585832834243774},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.3213112950325012},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.300706684589386},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.11080840229988098}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8229069709777832},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.8160606622695923},{"id":"https://openalex.org/C15759828","wikidata":"https://www.wikidata.org/wiki/Q165074","display_name":"Parallax","level":2,"score":0.7532254457473755},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6617966890335083},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.655454695224762},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5919144749641418},{"id":"https://openalex.org/C93996380","wikidata":"https://www.wikidata.org/wiki/Q44127","display_name":"Server","level":2,"score":0.5334759950637817},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.46724677085876465},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43450090289115906},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.41868507862091064},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41585832834243774},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.3213112950325012},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.300706684589386},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.11080840229988098},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3302424.3303957","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3302424.3303957","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Fourteenth EuroSys Conference 2019","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1442374986","https://openalex.org/W1506342804","https://openalex.org/W1586532344","https://openalex.org/W1686810756","https://openalex.org/W1978516206","https://openalex.org/W2021216254","https://openalex.org/W2057332538","https://openalex.org/W2083842231","https://openalex.org/W2117539524","https://openalex.org/W2141561793","https://openalex.org/W2152175008","https://openalex.org/W2183341477","https://openalex.org/W2186615578","https://openalex.org/W2194775991","https://openalex.org/W2259472270","https://openalex.org/W2327501763","https://openalex.org/W2336650964","https://openalex.org/W2423557781","https://openalex.org/W2519887557","https://openalex.org/W2525778437","https://openalex.org/W2593245696","https://openalex.org/W2622263826","https://openalex.org/W2739720758","https://openalex.org/W2765627325","https://openalex.org/W2769856846","https://openalex.org/W2783487361","https://openalex.org/W2787998955","https://openalex.org/W2802166112","https://openalex.org/W2803543130","https://openalex.org/W2807147113","https://openalex.org/W2883830791","https://openalex.org/W2884711234","https://openalex.org/W2899771611","https://openalex.org/W2951714314","https://openalex.org/W2962950660","https://openalex.org/W2964299589","https://openalex.org/W2964324519","https://openalex.org/W3101104221"],"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/W4391547476","https://openalex.org/W2076915000"],"abstract_inverted_index":{"The":[0,180],"employment":[1],"of":[2,85,92,112,131],"high-performance":[3],"servers":[4],"and":[5,29,124,152,174,192],"GPU":[6],"accelerators":[7],"for":[8,51,58,169,183],"training":[9,61,78,107,147,181],"deep":[10,20],"neural":[11],"network":[12],"models":[13,40,80,154,171,187],"have":[14,31],"greatly":[15],"accelerated":[16],"recent":[17],"advances":[18],"in":[19,41,90],"learning":[21],"(DL).":[22],"DL":[23,35,47],"frameworks,":[24],"such":[25],"as":[26],"TensorFlow,":[27],"MXNet,":[28],"Caffe2,":[30],"emerged":[32],"to":[33,37,82,87,127,135,165,190],"assist":[34],"researchers":[36],"train":[38],"their":[39],"a":[42,101,117],"distributed":[43,60],"manner.":[44],"Although":[45],"current":[46,71],"frameworks":[48,72],"scale":[49],"well":[50],"image":[52,185],"classification":[53,186],"models,":[54],"there":[55],"remain":[56],"opportunities":[57],"scalable":[59,146],"on":[62,77,149],"natural":[63],"language":[64],"processing":[65],"(NLP)":[66],"models.":[67],"We":[68],"found":[69],"that":[70,103,120,140],"show":[73,139],"relatively":[74],"low":[75],"scalability":[76],"NLP":[79,170],"due":[81],"the":[83,88,110,129,136,184],"lack":[84],"consideration":[86],"difference":[89],"sparsity":[91,111],"model":[93,113],"parameters.":[94,114],"In":[95],"this":[96],"paper,":[97],"we":[98],"propose":[99],"Parallax,":[100],"framework":[102],"optimizes":[104],"data":[105,132],"parallel":[106],"by":[108],"utilizing":[109],"Parallax":[115,141,162],"introduces":[116],"hybrid":[118],"approach":[119],"combines":[121],"Parameter":[122],"Server":[123],"AllReduce":[125],"architectures":[126],"optimize":[128],"amount":[130],"transfer":[133],"according":[134],"sparsity.":[137],"Experiments":[138],"built":[142],"atop":[143],"Tensor-Flow":[144],"achieves":[145,163],"throughput":[148],"both":[150],"dense":[151],"sparse":[153],"while":[155],"requiring":[156],"little":[157],"effort":[158],"from":[159],"its":[160],"users.":[161],"up":[164],"2.8x,":[166],"6.02x":[167],"speedup":[168],"than":[172,195],"TensorFlow":[173],"Horovod":[175,191],"with":[176],"48":[177],"GPUs,":[178],"respectively.":[179],"speed":[182],"is":[188],"equal":[189],"1.53x":[193],"faster":[194],"TensorFlow.":[196]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":13},{"year":2021,"cited_by_count":16},{"year":2020,"cited_by_count":15}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2019-01-01T00:00:00"}
