{"id":"https://openalex.org/W3038104246","doi":"https://doi.org/10.1145/3369583.3392681","title":"FFT-based Gradient Sparsification for the Distributed Training of Deep Neural Networks","display_name":"FFT-based Gradient Sparsification for the Distributed Training of Deep Neural Networks","publication_year":2020,"publication_date":"2020-06-21","ids":{"openalex":"https://openalex.org/W3038104246","doi":"https://doi.org/10.1145/3369583.3392681","mag":"3038104246"},"language":"en","primary_location":{"id":"doi:10.1145/3369583.3392681","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3369583.3392681","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th International Symposium on High-Performance Parallel and Distributed Computing","raw_type":"proceedings-article"},"type":"conference-paper","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/A5010181097","display_name":"Linnan Wang","orcid":"https://orcid.org/0000-0001-6114-7098"},"institutions":[{"id":"https://openalex.org/I27804330","display_name":"Brown University","ror":"https://ror.org/05gq02987","country_code":"US","type":"education","lineage":["https://openalex.org/I27804330"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Linnan Wang","raw_affiliation_strings":["Brown University, Providence, RI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Brown University, Providence, RI, USA","institution_ids":["https://openalex.org/I27804330"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027254893","display_name":"Wei Wu","orcid":"https://orcid.org/0000-0002-2750-6365"},"institutions":[{"id":"https://openalex.org/I1343871089","display_name":"Los Alamos National Laboratory","ror":"https://ror.org/01e41cf67","country_code":"US","type":"facility","lineage":["https://openalex.org/I1330989302","https://openalex.org/I1343871089","https://openalex.org/I198811213","https://openalex.org/I4210120050"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Wu","raw_affiliation_strings":["Los Alamos National Laboratory, Los Alamos, NM, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Los Alamos National Laboratory, Los Alamos, NM, USA","institution_ids":["https://openalex.org/I1343871089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101898224","display_name":"Junyu Zhang","orcid":"https://orcid.org/0000-0003-2194-9664"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]},{"id":"https://openalex.org/I4210101327","display_name":"Twin Cities Orthopedics","ror":"https://ror.org/01en4s460","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I4210101327"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Junyu Zhang","raw_affiliation_strings":["University of Minnesota, Twin Cities, Minneapolis, MN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Minnesota, Twin Cities, Minneapolis, MN, USA","institution_ids":["https://openalex.org/I130238516","https://openalex.org/I4210101327"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100338967","display_name":"Hang Liu","orcid":"https://orcid.org/0000-0002-5246-8399"},"institutions":[{"id":"https://openalex.org/I108468826","display_name":"Stevens Institute of Technology","ror":"https://ror.org/02z43xh36","country_code":"US","type":"education","lineage":["https://openalex.org/I108468826"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hang Liu","raw_affiliation_strings":["Stevens Institute of Technology, Hoboken, NJ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stevens Institute of Technology, Hoboken, NJ, USA","institution_ids":["https://openalex.org/I108468826"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010055736","display_name":"George Bosilca","orcid":"https://orcid.org/0000-0003-2411-8495"},"institutions":[{"id":"https://openalex.org/I75027704","display_name":"University of Tennessee at Knoxville","ror":"https://ror.org/020f3ap87","country_code":"US","type":"education","lineage":["https://openalex.org/I75027704"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"George Bosilca","raw_affiliation_strings":["University of Tennessee, Knoxville, TN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Tennessee, Knoxville, TN, USA","institution_ids":["https://openalex.org/I75027704"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086347882","display_name":"Maurice Herlihy","orcid":"https://orcid.org/0000-0002-3059-8926"},"institutions":[{"id":"https://openalex.org/I27804330","display_name":"Brown University","ror":"https://ror.org/05gq02987","country_code":"US","type":"education","lineage":["https://openalex.org/I27804330"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Maurice Herlihy","raw_affiliation_strings":["Brown University, Providence, RI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Brown University, Providence, RI, USA","institution_ids":["https://openalex.org/I27804330"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036529548","display_name":"Rodrigo Fonseca","orcid":"https://orcid.org/0000-0001-9662-2661"},"institutions":[{"id":"https://openalex.org/I27804330","display_name":"Brown University","ror":"https://ror.org/05gq02987","country_code":"US","type":"education","lineage":["https://openalex.org/I27804330"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rodrigo Fonseca","raw_affiliation_strings":["Brown University, Providence, RI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Brown University, Providence, RI, USA","institution_ids":["https://openalex.org/I27804330"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.7197,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.8769292,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"113","last_page":"124"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9998000264167786,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9998000264167786,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9997000098228455,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/lossy-compression","display_name":"Lossy compression","score":0.9045698046684265},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8236793279647827},{"id":"https://openalex.org/keywords/lossless-compression","display_name":"Lossless compression","score":0.7631798386573792},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.6534382104873657},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5388628244400024},{"id":"https://openalex.org/keywords/clipping","display_name":"Clipping (morphology)","score":0.46816062927246094},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4622603952884674},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.43998515605926514},{"id":"https://openalex.org/keywords/fast-fourier-transform","display_name":"Fast Fourier transform","score":0.4210051894187927},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.39225703477859497},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.3840588331222534},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3200054168701172},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.31705260276794434}],"concepts":[{"id":"https://openalex.org/C165021410","wikidata":"https://www.wikidata.org/wiki/Q55564","display_name":"Lossy compression","level":2,"score":0.9045698046684265},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8236793279647827},{"id":"https://openalex.org/C81081738","wikidata":"https://www.wikidata.org/wiki/Q55542","display_name":"Lossless compression","level":3,"score":0.7631798386573792},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.6534382104873657},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5388628244400024},{"id":"https://openalex.org/C2776848632","wikidata":"https://www.wikidata.org/wiki/Q853463","display_name":"Clipping (morphology)","level":2,"score":0.46816062927246094},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4622603952884674},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.43998515605926514},{"id":"https://openalex.org/C75172450","wikidata":"https://www.wikidata.org/wiki/Q623950","display_name":"Fast Fourier transform","level":2,"score":0.4210051894187927},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.39225703477859497},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.3840588331222534},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3200054168701172},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31705260276794434},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3369583.3392681","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3369583.3392681","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th International Symposium on High-Performance Parallel and Distributed Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W809736386","https://openalex.org/W1580804245","https://openalex.org/W1584046620","https://openalex.org/W1825216778","https://openalex.org/W1992208280","https://openalex.org/W2058942559","https://openalex.org/W2083842231","https://openalex.org/W2155893237","https://openalex.org/W2168231600","https://openalex.org/W2186615578","https://openalex.org/W2194775991","https://openalex.org/W2274287116","https://openalex.org/W2298503502","https://openalex.org/W2402144811","https://openalex.org/W2407022425","https://openalex.org/W2486202470","https://openalex.org/W2489529491","https://openalex.org/W2562749854","https://openalex.org/W2580688187","https://openalex.org/W2750822049","https://openalex.org/W2769644379","https://openalex.org/W2883109957","https://openalex.org/W2911892981","https://openalex.org/W2949615858","https://openalex.org/W2962786581","https://openalex.org/W2963470657","https://openalex.org/W2963540381","https://openalex.org/W2963766684","https://openalex.org/W2963803379","https://openalex.org/W2964004663","https://openalex.org/W2964174152","https://openalex.org/W2964299589","https://openalex.org/W2964350391","https://openalex.org/W2985108934","https://openalex.org/W3103572230","https://openalex.org/W3103894541","https://openalex.org/W4301361180"],"related_works":["https://openalex.org/W2385628723","https://openalex.org/W2547124190","https://openalex.org/W3180760233","https://openalex.org/W3035703949","https://openalex.org/W4247601675","https://openalex.org/W1970394887","https://openalex.org/W755971114","https://openalex.org/W2118338613","https://openalex.org/W1982468865","https://openalex.org/W4313046148"],"abstract_inverted_index":{"The":[0,47],"performance":[1,16,136],"and":[2,66,114,128,131],"efficiency":[3],"of":[4,7,17,80],"distributed":[5],"training":[6],"Deep":[8],"Neural":[9],"Networks":[10],"(DNN)":[11],"highly":[12],"depend":[13],"on":[14],"the":[15,69,78,100],"gradient":[18],"averaging":[19],"among":[20],"participating":[21],"processes,":[22],"a":[23,91,106,122],"step":[24],"bound":[25],"by":[26],"communication":[27,36],"costs.":[28],"There":[29],"are":[30,132],"two":[31],"major":[32],"approaches":[33,75],"to":[34,105,112,134],"reduce":[35,44,77],"overhead:":[37],"overlap":[38],"communications":[39,45],"with":[40],"computations":[41],"(lossless),":[42],"or":[43],"(lossy).":[46],"lossless":[48],"solution":[49],"works":[50],"well":[51],"for":[52,71],"linear":[53],"neural":[54],"architectures,":[55],"e.g.":[56],"VGG,":[57],"AlexNet,":[58],"but":[59],"more":[60,84],"recent":[61],"networks":[62],"such":[63,72],"as":[64],"ResNet":[65],"Inception":[67],"limit":[68],"opportunity":[70],"overlapping.":[73],"Therefore,":[74],"that":[76,96],"amount":[79],"data":[81],"(lossy)":[82],"become":[83],"suitable.":[85],"In":[86],"this":[87],"paper,":[88],"we":[89],"present":[90],"novel,":[92],"explainable":[93],"lossy":[94],"method":[95],"sparsifies":[97],"gradients":[98],"in":[99,103,137],"frequency":[101],"domain,":[102],"addition":[104],"new":[107],"range-based":[108],"float":[109],"point":[110],"representation":[111],"quantize":[113],"further":[115],"compress":[116],"gradients.":[117],"These":[118],"dynamic":[119],"techniques":[120],"strike":[121],"balance":[123],"between":[124],"compression":[125],"ratio,":[126],"accuracy,":[127],"computational":[129],"overhead,":[130],"optimized":[133],"maximize":[135],"heterogeneous":[138],"environments.":[139]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":5}],"updated_date":"2026-08-19T15:00:24.278416","created_date":"2025-10-10T00:00:00"}
