{"id":"https://openalex.org/W3084163953","doi":"https://doi.org/10.1109/hpcs48598.2019.9188164","title":"The impact of the AC922 Architecture on Performance of Deep Neural Network Training","display_name":"The impact of the AC922 Architecture on Performance of Deep Neural Network Training","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W3084163953","doi":"https://doi.org/10.1109/hpcs48598.2019.9188164","mag":"3084163953"},"language":"en","primary_location":{"id":"doi:10.1109/hpcs48598.2019.9188164","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hpcs48598.2019.9188164","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Conference on High Performance Computing &amp; Simulation (HPCS)","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/A5010035863","display_name":"Pawe\u0142 Ro\u015bciszewski","orcid":"https://orcid.org/0000-0002-7070-1684"},"institutions":[{"id":"https://openalex.org/I169333911","display_name":"Gda\u0144sk University of Technology","ror":"https://ror.org/006x4sc24","country_code":"PL","type":"education","lineage":["https://openalex.org/I169333911"]}],"countries":["PL"],"is_corresponding":true,"raw_author_name":"Pawel Rosciszewski","raw_affiliation_strings":["Faculty of Electronics, Telecommunications and Informatics, Gda\u0144sk University of Technology, Poland"],"affiliations":[{"raw_affiliation_string":"Faculty of Electronics, Telecommunications and Informatics, Gda\u0144sk University of Technology, Poland","institution_ids":["https://openalex.org/I169333911"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090013651","display_name":"Micha\u0142 Iwa\u0144ski","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Michal Iwanski","raw_affiliation_strings":["IBM Systems, IBM, Poland"],"affiliations":[{"raw_affiliation_string":"IBM Systems, IBM, Poland","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080295497","display_name":"Pawe\u0142 Czarnul","orcid":"https://orcid.org/0000-0002-4918-9196"},"institutions":[{"id":"https://openalex.org/I169333911","display_name":"Gda\u0144sk University of Technology","ror":"https://ror.org/006x4sc24","country_code":"PL","type":"education","lineage":["https://openalex.org/I169333911"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Pawel Czarnul","raw_affiliation_strings":["Faculty of Electronics, Telecommunications and Informatics, Gda\u0144sk University of Technology, Poland"],"affiliations":[{"raw_affiliation_string":"Faculty of Electronics, Telecommunications and Informatics, Gda\u0144sk University of Technology, Poland","institution_ids":["https://openalex.org/I169333911"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5010035863"],"corresponding_institution_ids":["https://openalex.org/I169333911"],"apc_list":null,"apc_paid":null,"fwci":0.2024,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.57470769,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"666","last_page":"673"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":1.0,"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":1.0,"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9977999925613403,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9972000122070312,"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":0.8559072017669678},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.7476546168327332},{"id":"https://openalex.org/keywords/workstation","display_name":"Workstation","score":0.6714880466461182},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.6232826113700867},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6042933464050293},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.5523345470428467},{"id":"https://openalex.org/keywords/supercomputer","display_name":"Supercomputer","score":0.5337396860122681},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5271634459495544},{"id":"https://openalex.org/keywords/ibm","display_name":"IBM","score":0.506304144859314},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.47322240471839905},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4669789969921112},{"id":"https://openalex.org/keywords/instruction-set","display_name":"Instruction set","score":0.45557302236557007},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.44647327065467834},{"id":"https://openalex.org/keywords/profiling","display_name":"Profiling (computer programming)","score":0.4369199872016907},{"id":"https://openalex.org/keywords/gpu-cluster","display_name":"GPU cluster","score":0.43230748176574707},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.4267788231372833},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.4162443280220032},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.4101528227329254},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.4047323763370514},{"id":"https://openalex.org/keywords/cuda","display_name":"CUDA","score":0.21680232882499695},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.212471604347229},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.1606898009777069}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8559072017669678},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.7476546168327332},{"id":"https://openalex.org/C67953723","wikidata":"https://www.wikidata.org/wiki/Q192525","display_name":"Workstation","level":2,"score":0.6714880466461182},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.6232826113700867},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6042933464050293},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.5523345470428467},{"id":"https://openalex.org/C83283714","wikidata":"https://www.wikidata.org/wiki/Q121117","display_name":"Supercomputer","level":2,"score":0.5337396860122681},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5271634459495544},{"id":"https://openalex.org/C70388272","wikidata":"https://www.wikidata.org/wiki/Q5968558","display_name":"IBM","level":2,"score":0.506304144859314},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.47322240471839905},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4669789969921112},{"id":"https://openalex.org/C202491316","wikidata":"https://www.wikidata.org/wiki/Q272683","display_name":"Instruction set","level":2,"score":0.45557302236557007},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.44647327065467834},{"id":"https://openalex.org/C187191949","wikidata":"https://www.wikidata.org/wiki/Q1138496","display_name":"Profiling (computer programming)","level":2,"score":0.4369199872016907},{"id":"https://openalex.org/C2781335571","wikidata":"https://www.wikidata.org/wiki/Q2633544","display_name":"GPU cluster","level":3,"score":0.43230748176574707},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4267788231372833},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4162443280220032},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.4101528227329254},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.4047323763370514},{"id":"https://openalex.org/C2778119891","wikidata":"https://www.wikidata.org/wiki/Q477690","display_name":"CUDA","level":2,"score":0.21680232882499695},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.212471604347229},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.1606898009777069},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C171250308","wikidata":"https://www.wikidata.org/wiki/Q11468","display_name":"Nanotechnology","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/hpcs48598.2019.9188164","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hpcs48598.2019.9188164","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Conference on High Performance Computing &amp; Simulation (HPCS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.4399999976158142,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1667652561","https://openalex.org/W1686810756","https://openalex.org/W1922655562","https://openalex.org/W2163605009","https://openalex.org/W2179888866","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2271840356","https://openalex.org/W2563468918","https://openalex.org/W2621496142","https://openalex.org/W2751021288","https://openalex.org/W2761105295","https://openalex.org/W2775461895","https://openalex.org/W2791673912","https://openalex.org/W2796013597","https://openalex.org/W2919115771","https://openalex.org/W2953106684","https://openalex.org/W3101543398","https://openalex.org/W3103169341","https://openalex.org/W3127686677","https://openalex.org/W4302296459","https://openalex.org/W6620707391","https://openalex.org/W6637151318","https://openalex.org/W6637373629","https://openalex.org/W6640090968","https://openalex.org/W6684191040","https://openalex.org/W6686164453","https://openalex.org/W6687483927","https://openalex.org/W6694517276","https://openalex.org/W6746932923"],"related_works":["https://openalex.org/W2384867379","https://openalex.org/W3214280620","https://openalex.org/W2329539859","https://openalex.org/W3191490922","https://openalex.org/W2152751187","https://openalex.org/W2134581225","https://openalex.org/W1582436825","https://openalex.org/W3048263112","https://openalex.org/W3127907983","https://openalex.org/W3168598731"],"abstract_inverted_index":{"Practical":[0],"deep":[1,45],"learning":[2],"applications":[3],"require":[4],"more":[5,7],"and":[6,53,65,68],"computing":[8,11],"power.":[9],"New":[10],"architectures":[12],"emerge,":[13],"specifically":[14],"designed":[15],"for":[16,163],"the":[17,22,72,80,97,113,118,127,133],"artificial":[18],"intelligence":[19],"applications,":[20,49],"including":[21,50],"IBM":[23],"Power":[24],"System":[25],"AC922.":[26],"In":[27,146],"this":[28],"paper":[29],"we":[30],"confront":[31],"an":[32],"AC922":[33,98],"(8335-GTG)":[34],"server":[35],"equipped":[36],"with":[37,43,71],"4":[38,144],"NVIDIA":[39],"Volta":[40],"V100":[41],"GPUs":[42,84,160],"selected":[44],"neural":[46],"network":[47],"training":[48],"four":[51],"convolutional":[52],"one":[54],"recurrent":[55],"model.":[56],"We":[57],"report":[58],"performance":[59,123,155],"results":[60,73,94],"depending":[61,156],"on":[62,79,126,132,157],"batch":[63],"sizes":[64],"GPU":[66],"selection":[67],"compare":[69],"them":[70],"from":[74],"another":[75],"contemporary":[76],"workstation":[77],"based":[78],"same":[81],"set":[82],"of":[83],"-":[85],"NVIDIA\u00ae":[86],"DGX":[87],"Station":[88],"<sup":[89],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[90],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">\u2122</sup>":[91],".":[92],"The":[93,122],"show":[95],"that":[96,112],"performs":[99],"better":[100],"in":[101,154],"all":[102],"tested":[103],"configurations,":[104],"achieving":[105],"improvements":[106],"up":[107,142],"to":[108,117,143],"10.3%.":[109],"Profiling":[110],"indicates":[111],"improvement":[114],"is":[115,150],"due":[116],"efficient":[119],"I/O":[120],"pipeline.":[121],"differences":[124],"depend":[125],"specific":[128],"model,":[129],"rather":[130],"than":[131],"model":[134],"class":[135],"(RNN/CNN).":[136],"Both":[137],"systems":[138],"offer":[139],"good":[140],"scalability":[141],"GPUs.":[145],"certain":[147],"cases":[148],"there":[149],"a":[151],"significant":[152],"difference":[153],"exactly":[158],"which":[159],"are":[161],"used":[162],"computations.":[164]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}