{"id":"https://openalex.org/W3000457014","doi":"https://doi.org/10.1109/ipccc47392.2019.8958752","title":"A C++ Library for Tensor Decomposition","display_name":"A C++ Library for Tensor Decomposition","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W3000457014","doi":"https://doi.org/10.1109/ipccc47392.2019.8958752","mag":"3000457014"},"language":"en","primary_location":{"id":"doi:10.1109/ipccc47392.2019.8958752","doi":"https://doi.org/10.1109/ipccc47392.2019.8958752","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ipccc47392.2019.8958752","pdf_url":null,"source":{"id":"https://openalex.org/S4210175320","display_name":"IEEE International Performance, Computing, and Communications Conference","issn_l":"1097-2641","issn":["1097-2641","2374-9628"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 38th International Performance Computing and Communications Conference (IPCCC)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5077802060","display_name":"Jiapeng Huang","orcid":"https://orcid.org/0000-0001-5722-1064","observed_orcids":["https://orcid.org/0000-0001-5722-1064"]},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiapeng Huang","raw_affiliation_strings":["Shanghai Jiao Tong University*"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University*","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072308822","display_name":"Linghe Kong","orcid":"https://orcid.org/0000-0001-9266-3044","observed_orcids":["https://orcid.org/0000-0001-9266-3044"]},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Linghe Kong","raw_affiliation_strings":["Shanghai Jiao Tong University*"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University*","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100405233","display_name":"Xiao-Yang Liu","orcid":"https://orcid.org/0000-0002-9532-1709","observed_orcids":["https://orcid.org/0000-0002-9532-1709"]},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiao-Yang Liu","raw_affiliation_strings":["Columbia University\u2020"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Columbia University\u2020","institution_ids":["https://openalex.org/I78577930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029996997","display_name":"Wenhao Qu","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenhao Qu","raw_affiliation_strings":["Shanghai Jiao Tong University*"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University*","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100428808","display_name":"Guihai Chen","orcid":"https://orcid.org/0000-0002-6934-1685","observed_orcids":["https://orcid.org/0000-0002-6934-1685","https://orcid.org/0009-0002-7903-853X"]},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guihai Chen","raw_affiliation_strings":["Shanghai Jiao Tong University*"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University*","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5772,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.50660793,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"2"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.0,"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/T10320","display_name":"Neural Networks and Applications","score":0.0,"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/tensor-decomposition","display_name":"tensor decomposition","score":0.902999997138977},{"id":"https://openalex.org/keywords/c-library","display_name":"C++ library","score":0.9440000057220459},{"id":"https://openalex.org/keywords/canonical-polyadic-decomposition","display_name":"canonical polyadic decomposition","score":0.9120000004768372},{"id":"https://openalex.org/keywords/tucker-decomposition","display_name":"Tucker decomposition","score":0.9980000257492065},{"id":"https://openalex.org/keywords/tensor-train","display_name":"tensor train","score":0.7739999890327454},{"id":"https://openalex.org/keywords/deep-learning-acceleration","display_name":"deep learning acceleration","score":0.593999981880188}],"concepts":[{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.7664073705673218},{"id":"https://openalex.org/C150202949","wikidata":"https://www.wikidata.org/wiki/Q107602","display_name":"Pointer (user interface)","level":2,"score":0.7108155488967896},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6737130284309387},{"id":"https://openalex.org/C2780365114","wikidata":"https://www.wikidata.org/wiki/Q169478","display_name":"MATLAB","level":2,"score":0.6004446148872375},{"id":"https://openalex.org/C2986737658","wikidata":"https://www.wikidata.org/wiki/Q30103009","display_name":"Tensor decomposition","level":3,"score":0.5967581272125244},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.5690127611160278},{"id":"https://openalex.org/C124007464","wikidata":"https://www.wikidata.org/wiki/Q428091","display_name":"Tensor contraction","level":3,"score":0.4775691628456116},{"id":"https://openalex.org/C459310","wikidata":"https://www.wikidata.org/wiki/Q117801","display_name":"Computational science","level":1,"score":0.4517289698123932},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.4397067129611969},{"id":"https://openalex.org/C2779791154","wikidata":"https://www.wikidata.org/wiki/Q258040","display_name":"Model transformation","level":3,"score":0.411668598651886},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.40664082765579224},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.34833356738090515},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32572662830352783},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.29595133662223816},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2552937865257263},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21869903802871704},{"id":"https://openalex.org/C51255310","wikidata":"https://www.wikidata.org/wiki/Q1163016","display_name":"Tensor product","level":2,"score":0.20885854959487915},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.1749666929244995},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ipccc47392.2019.8958752","doi":"https://doi.org/10.1109/ipccc47392.2019.8958752","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ipccc47392.2019.8958752","pdf_url":null,"source":{"id":"https://openalex.org/S4210175320","display_name":"IEEE International Performance, Computing, and Communications Conference","issn_l":"1097-2641","issn":["1097-2641","2374-9628"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 38th International Performance Computing and Communications Conference (IPCCC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W182691100","https://openalex.org/W2024165284","https://openalex.org/W2102182691","https://openalex.org/W2469230926","https://openalex.org/W2544822491","https://openalex.org/W2590246587"],"related_works":["https://openalex.org/W4387806182","https://openalex.org/W4408979225","https://openalex.org/W7092282007","https://openalex.org/W2731053425","https://openalex.org/W2890447054","https://openalex.org/W3009356518","https://openalex.org/W86406015","https://openalex.org/W2607448249","https://openalex.org/W3030415228","https://openalex.org/W3174786754"],"abstract_inverted_index":{"In":[0,133],"this":[1],"paper,":[2],"we":[3,64],"develop":[4],"a":[5,66,93,105],"new":[6],"library":[7],"TenDeC++":[8,14,136],"for":[9,125],"tensor":[10,17,79,86,131,145],"decompositions":[11],"in":[12,85,101,151,155],"C++.":[13],"supports":[15],"popular":[16],"decomposition":[18,117,138],"functions":[19],"including":[20],"Canonical":[21],"Polyadic,":[22],"Tucker,":[23],"tensor-train,":[24],"and":[25,44,55,80,119,140,153],"t-SVD,":[26],"assisting":[27],"C++":[28,46],"programmers":[29],"to":[30,114],"shorten":[31],"the":[32,41,48,72,76,97,107,116,127,148],"development":[33],"cycle":[34],"of":[35,62,99,130,144],"deep":[36],"learning":[37],"applications.":[38],"Compared":[39],"with":[40,147],"resource-intensive":[42],"Python":[43,152],"MATLAB,":[45,156],"has":[47],"nature":[49],"advantages":[50],"on":[51],"fast":[52],"running":[53],"time":[54,139],"high":[56],"compatibility.":[57],"To":[58],"further":[59],"explore":[60],"potentials":[61],"C++,":[63],"propose":[65],"novel":[67],"underlying":[68],"technology":[69],"PointerDefomer":[70,88],"leveraging":[71],"unique":[73],"pointer.":[74],"Since":[75],"transformation":[77,94,109],"between":[78],"size-specific":[81],"matrix":[82],"is":[83,121],"indispensable":[84],"decompositions,":[87],"can":[89,111],"virtually":[90],"achieve":[91],"such":[92],"by":[95],"controlling":[96],"movement":[98],"pointer":[100],"memory":[102,123],"address.":[103],"As":[104],"result,":[106],"conventional":[108],"steps":[110],"be":[112],"skipped":[113],"accelerate":[115],"process":[118],"there":[120],"no":[122],"needed":[124],"saving":[126],"intermediate":[128],"results":[129],"transformation.":[132],"our":[134],"experiment,":[135],"reduces":[137],"support":[141],"larger":[142],"size":[143],"compared":[146],"classic":[149],"Tensorly":[150],"TensorLab":[154],"respectively.":[157]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2020-01-23T00:00:00"}