{"id":"https://openalex.org/W3008843498","doi":"https://doi.org/10.1109/bigdata47090.2019.9006180","title":"Parallel Training via Computation Graph Transformation","display_name":"Parallel Training via Computation Graph Transformation","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3008843498","doi":"https://doi.org/10.1109/bigdata47090.2019.9006180","mag":"3008843498"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata47090.2019.9006180","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9006180","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","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/A5100455958","display_name":"Fei Wang","orcid":"https://orcid.org/0000-0003-3462-8472"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Fei Wang","raw_affiliation_strings":["Purdue University, West Lafayette, USA"],"affiliations":[{"raw_affiliation_string":"Purdue University, West Lafayette, USA","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103032013","display_name":"Guoyang Chen","orcid":"https://orcid.org/0000-0002-5531-8986"},"institutions":[{"id":"https://openalex.org/I4210095624","display_name":"Alibaba Group (United States)","ror":"https://ror.org/00rn0m335","country_code":"US","type":"company","lineage":["https://openalex.org/I4210095624","https://openalex.org/I45928872"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Guoyang Chen","raw_affiliation_strings":["Alibaba Group, Sunnyvale, USA"],"affiliations":[{"raw_affiliation_string":"Alibaba Group, Sunnyvale, USA","institution_ids":["https://openalex.org/I4210095624"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100426678","display_name":"Weifeng Zhang","orcid":"https://orcid.org/0000-0002-4529-1679"},"institutions":[{"id":"https://openalex.org/I4210095624","display_name":"Alibaba Group (United States)","ror":"https://ror.org/00rn0m335","country_code":"US","type":"company","lineage":["https://openalex.org/I4210095624","https://openalex.org/I45928872"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Weifeng Zhang","raw_affiliation_strings":["Alibaba Group, Sunnyvale, USA"],"affiliations":[{"raw_affiliation_string":"Alibaba Group, Sunnyvale, USA","institution_ids":["https://openalex.org/I4210095624"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003153354","display_name":"Tiark Rompf","orcid":"https://orcid.org/0000-0002-2068-3238"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tiark Rompf","raw_affiliation_strings":["Purdue University, West Lafayette, USA"],"affiliations":[{"raw_affiliation_string":"Purdue University, West Lafayette, USA","institution_ids":["https://openalex.org/I219193219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100455958"],"corresponding_institution_ids":["https://openalex.org/I219193219"],"apc_list":null,"apc_paid":null,"fwci":0.2408,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.54278633,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"3430","last_page":"3439"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/T12292","display_name":"Graph Theory and Algorithms","score":0.991599977016449,"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/T11005","display_name":"Radiation Effects in Electronics","score":0.9869999885559082,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8676978945732117},{"id":"https://openalex.org/keywords/data-parallelism","display_name":"Data parallelism","score":0.649156928062439},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.6449642181396484},{"id":"https://openalex.org/keywords/compiler","display_name":"Compiler","score":0.48698005080223083},{"id":"https://openalex.org/keywords/porting","display_name":"Porting","score":0.48312732577323914},{"id":"https://openalex.org/keywords/task-parallelism","display_name":"Task parallelism","score":0.4821619391441345},{"id":"https://openalex.org/keywords/instruction-level-parallelism","display_name":"Instruction-level parallelism","score":0.4478592872619629},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.44204181432724},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4324706196784973},{"id":"https://openalex.org/keywords/executable","display_name":"Executable","score":0.4292997121810913},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39702051877975464},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3509581983089447},{"id":"https://openalex.org/keywords/parallelism","display_name":"Parallelism (grammar)","score":0.3205109238624573},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.3167794644832611},{"id":"https://openalex.org/keywords/software","display_name":"Software","score":0.12314960360527039}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8676978945732117},{"id":"https://openalex.org/C61483411","wikidata":"https://www.wikidata.org/wiki/Q3124522","display_name":"Data parallelism","level":3,"score":0.649156928062439},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.6449642181396484},{"id":"https://openalex.org/C169590947","wikidata":"https://www.wikidata.org/wiki/Q47506","display_name":"Compiler","level":2,"score":0.48698005080223083},{"id":"https://openalex.org/C106251023","wikidata":"https://www.wikidata.org/wiki/Q851989","display_name":"Porting","level":3,"score":0.48312732577323914},{"id":"https://openalex.org/C42992933","wikidata":"https://www.wikidata.org/wiki/Q691169","display_name":"Task parallelism","level":3,"score":0.4821619391441345},{"id":"https://openalex.org/C140763907","wikidata":"https://www.wikidata.org/wiki/Q2714055","display_name":"Instruction-level parallelism","level":3,"score":0.4478592872619629},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.44204181432724},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4324706196784973},{"id":"https://openalex.org/C160145156","wikidata":"https://www.wikidata.org/wiki/Q778586","display_name":"Executable","level":2,"score":0.4292997121810913},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39702051877975464},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3509581983089447},{"id":"https://openalex.org/C2781172179","wikidata":"https://www.wikidata.org/wiki/Q853109","display_name":"Parallelism (grammar)","level":2,"score":0.3205109238624573},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.3167794644832611},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.12314960360527039}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata47090.2019.9006180","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9006180","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","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":36,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1598866093","https://openalex.org/W2036551003","https://openalex.org/W2055312318","https://openalex.org/W2153185479","https://openalex.org/W2163605009","https://openalex.org/W2168231600","https://openalex.org/W2186615578","https://openalex.org/W2338908902","https://openalex.org/W2402144811","https://openalex.org/W2805566098","https://openalex.org/W2899771611","https://openalex.org/W2953384591","https://openalex.org/W2963959650","https://openalex.org/W2964121744","https://openalex.org/W2966177220","https://openalex.org/W2991040477","https://openalex.org/W3037338728","https://openalex.org/W3037847693","https://openalex.org/W4246166885","https://openalex.org/W4248722156","https://openalex.org/W4288357791","https://openalex.org/W6631190155","https://openalex.org/W6635810480","https://openalex.org/W6684191040","https://openalex.org/W6684859321","https://openalex.org/W6686509673","https://openalex.org/W6703652217","https://openalex.org/W6713134421","https://openalex.org/W6747917562","https://openalex.org/W6749810332","https://openalex.org/W6752474621","https://openalex.org/W6753584990","https://openalex.org/W6756040250","https://openalex.org/W6756718674","https://openalex.org/W6762211661"],"related_works":["https://openalex.org/W4240606930","https://openalex.org/W2526302199","https://openalex.org/W2003935582","https://openalex.org/W2950520577","https://openalex.org/W3007272028","https://openalex.org/W2040503315","https://openalex.org/W168408236","https://openalex.org/W2494130044","https://openalex.org/W2105992728","https://openalex.org/W2009213655"],"abstract_inverted_index":{"Parallel":[0],"training":[1,51,90,145,159,165],"can":[2,92,180,217],"speed":[3],"up":[4],"the":[5,13,19,68,140,182],"convergence":[6],"of":[7,22,143,253],"machine":[8,38,77,151,195,221,239],"learning":[9,39,78,152,196,222,240],"models":[10],"via":[11,106,245],"splitting":[12],"workload":[14],"into":[15,185],"multiple":[16],"accelerators":[17],"by":[18,121,205,224],"wide":[20],"array":[21],"possible":[23],"parallel":[24,50,89,144,158,164,183,207],"paradigms":[25],"(e.g.,":[26,53],"data":[27,65],"parallelism,":[28,30,32],"model":[29],"attribute":[31],"and":[33,47,67,112,209,251,255],"pipelining":[34],"parallelism).":[35],"However,":[36],"most":[37,88],"frameworks":[40,79,197,223],"lack":[41],"sufficient":[42],"support":[43,72],"for":[44,60,124],"these":[45],"flexible":[46],"sometimes":[48],"complex":[49],"schemes":[52,166,184],"TensorFlow":[54],"does":[55],"not":[56],"provide":[57],"convenient":[58],"APIs":[59],"any":[61],"paradigm":[62],"other":[63],"than":[64],"parallelism),":[66],"engineering":[69,141],"effort":[70],"to":[71,114,146,220],"all":[73,76],"parallelisms":[74],"in":[75,133,167,213,238,249,258],"seems":[80],"gigantic.":[81],"In":[82],"this":[83],"paper,":[84],"we":[85],"demonstrate":[86],"that":[87,102,179],"designs/paradigms":[91],"be":[93,147,218],"abstracted":[94],"as":[95,199],"a":[96,134,168,174,234],"computation":[97,107,130,175,210],"graph":[98,108,131,176,211],"transformation":[99,177],"problem,":[100],"so":[101],"they":[103],"are":[104,118,190],"realized":[105],"duplication,":[109],"splitting,":[110],"augmentation,":[111],"assignment":[113],"different":[115],"accelerators,":[116],"which":[117,162,189,216],"then":[119,172],"connected":[120],"send/recv":[122],"channels":[123],"tensor":[125],"communications.":[126],"Furthermore,":[127],"conducting":[128],"such":[129],"transformations":[132,212],"por":[135],"table":[136],"I":[137],"R":[138],"allows":[139],"efforts":[142],"widely":[148],"applied":[149],"across":[150],"frameworks.":[153],"We":[154,171,201],"propose":[155],"an":[156],"extensible":[157],"search":[160],"space":[161],"describes":[163],"declarative":[169],"fashion.":[170],"implement":[173],"compiler":[178],"instantiate":[181],"explicit":[186],"execution":[187],"plans,":[188],"readily":[191],"executable":[192],"on":[193],"modern":[194],"(such":[198],"TensorFlow).":[200],"maximize":[202],"code":[203,243],"reuse":[204,244],"handling":[206],"configurations":[208],"extended":[214],"ONNX,":[215],"ported":[219],"adapting":[225],"their":[226],"existing":[227],"ONNX":[228],"frontend/backend":[229],"implementations.":[230],"Our":[231],"design":[232],"reflects":[233],"few":[235],"good":[236],"themes":[237],"frameworks,":[241],"including":[242],"powerful":[246],"IR":[247],"(as":[248,257],"MLIR)":[250],"separation":[252],"declaration":[254],"realization":[256],"Halide/TVM).":[259]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-25T21:42:39.735039","created_date":"2025-10-10T00:00:00"}