{"id":"https://openalex.org/W4206197665","doi":"https://doi.org/10.1109/tpds.2022.3140681","title":"Building High-throughput Neural Architecture Search Workflows via a Decoupled Fitness Prediction Engine","display_name":"Building High-throughput Neural Architecture Search Workflows via a Decoupled Fitness Prediction Engine","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4206197665","doi":"https://doi.org/10.1109/tpds.2022.3140681"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://doi.org/10.1109/tpds.2022.3140681","pdf_url":"https://ieeexplore.ieee.org/ielx7/71/9782123/09674227.pdf","source":{"id":"https://openalex.org/S97130795","display_name":"IEEE Transactions on Parallel and Distributed Systems","issn_l":"1045-9219","issn":["1045-9219","1558-2183","2161-9883"],"is_oa":false,"is_in_doaj":false,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","version":"publishedVersion","is_accepted":true,"is_published":true},"type":"article","type_crossref":"journal-article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://ieeexplore.ieee.org/ielx7/71/9782123/09674227.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5008030809","display_name":"Ariel Keller Rorabaugh","orcid":"https://orcid.org/0000-0002-9000-3701"},"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/I2799495847","https://openalex.org/I75027704"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ariel Keller Rorabaugh","raw_affiliation_string":"University of Tennessee at Knoxville, Knoxville, TN, USA","raw_affiliation_strings":["University of Tennessee at Knoxville, Knoxville, TN, USA"]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052995286","display_name":"Silvina Ca\u00edno\u2010Lores","orcid":"https://orcid.org/0000-0002-6922-0138"},"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/I2799495847","https://openalex.org/I75027704"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Silvina Caino-Lores","raw_affiliation_string":"Department of Electrical Engineering and Computer Science, The University of Tennessee Knoxville, 4292 Knoxville, Tennessee, United States, (e-mail: scainolo@utk.edu)","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, The University of Tennessee Knoxville, 4292 Knoxville, Tennessee, United States, (e-mail: scainolo@utk.edu)"]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084600563","display_name":"Travis Johnston","orcid":null},"institutions":[{"id":"https://openalex.org/I4210156795","display_name":"Strive Preparatory Schools","ror":"https://ror.org/04x1eer44","country_code":"US","type":"education","lineage":["https://openalex.org/I4210156795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Travis Johnston","raw_affiliation_string":"HPC, Striveworks, Austin, Texas, United States, (e-mail: j.travis.johnston@gmail.com)","raw_affiliation_strings":["HPC, Striveworks, Austin, Texas, United States, (e-mail: j.travis.johnston@gmail.com)"]},{"author_position":"last","author":{"id":"https://openalex.org/A5021912259","display_name":"Michela Taufer","orcid":null},"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/I2799495847","https://openalex.org/I75027704"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Michela Taufer","raw_affiliation_string":"University of Tennessee at Knoxville, Knoxville, TN, USA","raw_affiliation_strings":["University of Tennessee at Knoxville, Knoxville, TN, USA"]}],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"has_fulltext":false,"cited_by_count":4,"cited_by_percentile_year":{"min":86,"max":89},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"1"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Deep Learning in Computer Vision and Image Recognition","score":0.9995,"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":"Deep Learning in Computer Vision and Image Recognition","score":0.9995,"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/T11689","display_name":"Adversarial Robustness in Deep Learning Models","score":0.997,"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/T12026","display_name":"Explainable Artificial Intelligence","score":0.9958,"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":[{"keyword":"decoupled fitness prediction engine","score":0.6929},{"keyword":"architecture","score":0.3314},{"keyword":"search","score":0.2791},{"keyword":"high-throughput","score":0.25}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.85884434},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.7564117},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.6486222},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.60705435},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5846339},{"id":"https://openalex.org/C26713055","wikidata":"https://www.wikidata.org/wiki/Q245962","display_name":"Implementation","level":2,"score":0.5668996},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.5262227},{"id":"https://openalex.org/C83283714","wikidata":"https://www.wikidata.org/wiki/Q121117","display_name":"Supercomputer","level":2,"score":0.50764716},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47630513},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.45847324},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44358748},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.36886406},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.35658953},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.20752099},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.1830636},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.17021942},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.12760207},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":true,"landing_page_url":"https://doi.org/10.1109/tpds.2022.3140681","pdf_url":"https://ieeexplore.ieee.org/ielx7/71/9782123/09674227.pdf","source":{"id":"https://openalex.org/S97130795","display_name":"IEEE Transactions on Parallel and Distributed Systems","issn_l":"1045-9219","issn":["1045-9219","1558-2183","2161-9883"],"is_oa":false,"is_in_doaj":false,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","version":"publishedVersion","is_accepted":true,"is_published":true}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://doi.org/10.1109/tpds.2022.3140681","pdf_url":"https://ieeexplore.ieee.org/ielx7/71/9782123/09674227.pdf","source":{"id":"https://openalex.org/S97130795","display_name":"IEEE Transactions on Parallel and Distributed Systems","issn_l":"1045-9219","issn":["1045-9219","1558-2183","2161-9883"],"is_oa":false,"is_in_doaj":false,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","version":"publishedVersion","is_accepted":true,"is_published":true},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.55,"display_name":"Industry, innovation and infrastructure"}],"grants":[{"funder":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation","award_id":"1841758"}],"referenced_works_count":25,"referenced_works":["https://openalex.org/W2112796928","https://openalex.org/W2145607950","https://openalex.org/W2617106563","https://openalex.org/W2698640253","https://openalex.org/W2766065766","https://openalex.org/W2767077319","https://openalex.org/W2784287478","https://openalex.org/W2793130633","https://openalex.org/W2794792904","https://openalex.org/W2795726520","https://openalex.org/W2902851082","https://openalex.org/W2936503027","https://openalex.org/W2947137917","https://openalex.org/W2954234207","https://openalex.org/W2963946985","https://openalex.org/W2964024268","https://openalex.org/W2964081807","https://openalex.org/W2994450015","https://openalex.org/W3008968734","https://openalex.org/W3013696689","https://openalex.org/W3078478003","https://openalex.org/W3099343340","https://openalex.org/W3100191342","https://openalex.org/W4205108766","https://openalex.org/W4255158661"],"related_works":["https://openalex.org/W2546696010","https://openalex.org/W2320205417","https://openalex.org/W1485630101","https://openalex.org/W112744582","https://openalex.org/W2364921833","https://openalex.org/W2385146268","https://openalex.org/W2623111183","https://openalex.org/W2380023786","https://openalex.org/W3136744003","https://openalex.org/W1767718647"],"ngrams_url":"https://api.openalex.org/works/W4206197665/ngrams","abstract_inverted_index":{"Neural":[0,16],"networks":[1],"(NN)":[2],"are":[3],"used":[4],"in":[5,61],"high-performance":[6],"computing":[7],"and":[8,26,45,75,92,150],"high-throughput":[9],"analysis":[10],"to":[11,67,84,108,131,176,193],"extract":[12],"knowledge":[13],"from":[14],"datasets.":[15],"architecture":[17],"search":[18,58],"(NAS)":[19],"automates":[20],"NN":[21,38],"design":[22],"by":[23,171,188],"generating,":[24],"training,":[25],"analyzing":[27],"thousands":[28],"of":[29,43,70,104,118,139,153,174,191],"NNs.":[30,71,134],"However,":[31],"NAS":[32,73,87,107,128,156,164],"requires":[33],"massive":[34],"computational":[35],"power":[36],"for":[37],"training.":[39],"To":[40],"address":[41],"challenges":[42],"efficiency":[44],"scalability,":[46],"we":[47],"propose":[48],"PENGUIN,":[49],"a":[50,172,189],"decoupled":[51],"fitness":[52,69,103],"prediction":[53],"engine":[54,141],"that":[55,120,182],"informs":[56],"the":[57,102,116,137,154,159],"without":[59],"interfering":[60],"it.":[62],"PENGUIN":[63,83,96,167,183],"uses":[64],"parametric":[65,76,94],"modeling":[66,77],"predict":[68],"Existing":[72],"methods":[74],"functions":[78],"can":[79,124,168,184],"be":[80],"plugged":[81],"into":[82],"build":[85],"flexible":[86,93],"workflows.":[88],"Through":[89],"this":[90],"decoupling":[91],"modeling,":[95],"reduces":[97],"training":[98,110,186],"costs:":[99],"it":[100],"predicts":[101],"NNs,":[105],"enabling":[106],"terminate":[109],"NNs":[111,119,144],"early.":[112],"Early":[113],"termination":[114],"increases":[115],"number":[117],"fixed":[121],"compute":[122],"resources":[123],"evaluate,":[125],"thus":[126],"giving":[127],"additional":[129],"opportunity":[130],"find":[132],"better":[133],"We":[135],"assess":[136],"effectiveness":[138],"our":[140],"on":[142],"6,000":[143],"across":[145],"three":[146,151],"diverse":[147],"benchmark":[148],"datasets":[149],"state":[152],"art":[155],"implementations":[157,165],"using":[158],"Summit":[160],"supercomputer.":[161],"Augmenting":[162],"these":[163],"with":[166],"increase":[169],"throughput":[170],"factor":[173,190],"1.6":[175],"7.1.":[177],"Furthermore,":[178],"walltime":[179],"tests":[180],"indicate":[181],"reduce":[185],"time":[187],"2.5":[192],"5.3.":[194]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4206197665","counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3}],"updated_date":"2024-03-19T02:02:37.880825","created_date":"2022-01-26"}