{"id":"https://openalex.org/W2990935803","doi":"https://doi.org/10.1145/3358960.3379143","title":"DLBricks: Composable Benchmark Generation to Reduce Deep Learning Benchmarking Effort on CPUs","display_name":"DLBricks: Composable Benchmark Generation to Reduce Deep Learning Benchmarking Effort on CPUs","publication_year":2020,"publication_date":"2020-04-20","ids":{"openalex":"https://openalex.org/W2990935803","doi":"https://doi.org/10.1145/3358960.3379143","mag":"2990935803"},"language":"en","primary_location":{"id":"doi:10.1145/3358960.3379143","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3358960.3379143","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM/SPEC International Conference on Performance Engineering","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1911.07967","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Cheng Li","orcid":null},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Cheng Li","raw_affiliation_strings":["University of Illinois Urbana-Champaign, Urbana, IL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Illinois Urbana-Champaign, Urbana, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Abdul Dakkak","orcid":null},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Abdul Dakkak","raw_affiliation_strings":["University of Illinois Urbana-Champaign, URBANA, IL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Illinois Urbana-Champaign, URBANA, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jinjun Xiong","orcid":null},"institutions":[{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jinjun Xiong","raw_affiliation_strings":["IBM T. J. Watson Research Center, Yorktown Heights, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM T. J. Watson Research Center, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I4210114115"]}]},{"author_position":"last","author":{"id":null,"display_name":"Wen-mei Hwu","orcid":null},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wen-mei Hwu","raw_affiliation_strings":["University of Illinois Urbana-Champaign, Urbana, IL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Illinois Urbana-Champaign, Urbana, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"20","issue":null,"first_page":"202","last_page":"209"},"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9962000250816345,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9962000250816345,"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/benchmarking","display_name":"Benchmarking","score":0.9763000011444092},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.809499979019165},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.631600022315979},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.5203999876976013},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.47760000824928284},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4661000072956085},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.44279998540878296}],"concepts":[{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.9763000011444092},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8224999904632568},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.809499979019165},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.631600022315979},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.5203999876976013},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5116000175476074},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5101000070571899},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.47760000824928284},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4661000072956085},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.44279998540878296},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.3882000148296356},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.3691999912261963},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3499999940395355},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.33309999108314514},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31049999594688416},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.3070000112056732},{"id":"https://openalex.org/C2778915421","wikidata":"https://www.wikidata.org/wiki/Q3643177","display_name":"Performance improvement","level":2,"score":0.3059000074863434},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.29679998755455017},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.28349998593330383},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.2831999957561493},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.2646999955177307},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.2572999894618988}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3358960.3379143","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3358960.3379143","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM/SPEC International Conference on Performance Engineering","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1911.07967","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1911.07967","pdf_url":"https://arxiv.org/pdf/1911.07967","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1911.07967","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1911.07967","pdf_url":"https://arxiv.org/pdf/1911.07967","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W2127099598","https://openalex.org/W2395579298","https://openalex.org/W2515080096","https://openalex.org/W2783444794","https://openalex.org/W2796013597","https://openalex.org/W6753278433"],"related_works":[],"abstract_inverted_index":{"The":[0],"past":[1],"few":[2],"years":[3],"have":[4],"seen":[5],"a":[6,15,56,82,103],"surge":[7],"of":[8,18,72,91,105,118,145],"applying":[9],"Deep":[10],"Learning":[11],"(DL)":[12],"models":[13,31,101,156,178],"for":[14,175],"wide":[16],"array":[17],"tasks":[19,160],"such":[20],"as":[21],"image":[22],"classification,":[23],"object":[24],"detection,":[25],"machine":[26],"translation,":[27],"etc.":[28],"While":[29],"DL":[30,96,100,148,159,177],"provide":[32],"an":[33,171],"opportunity":[34],"to":[35,47,69,192],"solve":[36],"otherwise":[37],"intractable":[38],"tasks,":[39],"their":[40],"adoption":[41],"relies":[42],"on":[43,161,197],"them":[44],"being":[45],"optimized":[46],"meet":[48],"target":[49],"latency":[50],"and":[51,74,94,109,127,179,190],"resource":[52],"requirements.":[53],"Benchmarking":[54],"is":[55,131],"key":[57],"step":[58],"in":[59,66],"this":[60],"process":[61],"but":[62],"has":[63],"been":[64],"hampered":[65],"part":[67],"due":[68],"the":[70,89,111,116,119,128,138,143,176,181],"lack":[71],"representative":[73,147,163],"up-to-date":[75,136],"benchmarking":[76,129,182,194],"suites.":[77],"This":[78],"paper":[79],"proposes":[80],"DLBricks,":[81],"composable":[83],"benchmark":[84],"generation":[85],"design":[86],"that":[87,168],"reduces":[88,180],"effort":[90],"developing,":[92],"maintaining,":[93],"running":[95],"benchmarks.":[97,121],"DLBricks":[98,133,152,169],"decomposes":[99],"into":[102],"set":[104],"unique":[106],"runnable":[107],"networks":[108],"constructs":[110],"original":[112],"model's":[113],"performance":[114,117,173],"using":[115,153],"generated":[120,125],"Since":[122],"benchmarks":[123],"are":[124],"automatically":[126],"time":[130,183,195],"minimized,":[132],"can":[134],"keep":[135],"with":[137],"latest":[139],"proposed":[140],"models,":[141],"relieving":[142],"pressure":[144],"selecting":[146],"models.":[149],"We":[150,166],"evaluate":[151],"50":[154],"MXNet":[155],"spanning":[157],"5":[158],"4":[162],"CPU":[164],"systems.":[165],"show":[167],"provides":[170],"accurate":[172],"estimate":[174],"across":[184],"systems":[185],"(e.g.":[186],"within":[187],"95%":[188],"accuracy":[189],"up":[191],"4.4\u00d7":[193],"speedup":[196],"Amazon":[198],"EC2":[199],"c5.xlarge).":[200]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2019-12-05T00:00:00"}
