{"id":"https://openalex.org/W2080135560","doi":"https://doi.org/10.1145/2093973.2094002","title":"Fast GPU-based locality sensitive hashing for k-nearest neighbor computation","display_name":"Fast GPU-based locality sensitive hashing for k-nearest neighbor computation","publication_year":2011,"publication_date":"2011-11-01","ids":{"openalex":"https://openalex.org/W2080135560","doi":"https://doi.org/10.1145/2093973.2094002","mag":"2080135560"},"language":"en","primary_location":{"id":"doi:10.1145/2093973.2094002","doi":"https://doi.org/10.1145/2093973.2094002","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2093973.2094002","pdf_url":null,"source":{"id":"https://openalex.org/S7407088447","display_name":"ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","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/A5076812698","display_name":"Jia Pan","orcid":"https://orcid.org/0000-0001-9003-2054","observed_orcids":["https://orcid.org/0000-0001-9003-2054"]},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jia Pan","raw_affiliation_strings":["UNC Chapel Hill"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"UNC Chapel Hill","institution_ids":["https://openalex.org/I114027177"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004194238","display_name":"Dinesh Manocha","orcid":"https://orcid.org/0000-0001-7047-9801","observed_orcids":["https://orcid.org/0000-0001-7047-9801"]},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dinesh Manocha","raw_affiliation_strings":["UNC Chapel Hill"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"UNC Chapel Hill","institution_ids":["https://openalex.org/I114027177"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I114027177"],"apc_list":null,"apc_paid":null,"fwci":5.9395,"has_fulltext":false,"cited_by_count":126,"citation_normalized_percentile":{"value":0.9744677,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"211","last_page":"220"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9824000000953674,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9824000000953674,"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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.00279999990016222,"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":0.001500000013038516,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/locality-sensitive-hashing","display_name":"locality-sensitive hashing","score":0.9750000238418579},{"id":"https://openalex.org/keywords/approximate-nearest-neighbor-search","display_name":"approximate nearest neighbor search","score":0.7889999747276306},{"id":"https://openalex.org/keywords/k-nearest-neighbors","display_name":"k-nearest neighbors","score":0.7289999723434448},{"id":"https://openalex.org/keywords/gpu-acceleration","display_name":"GPU acceleration","score":0.7480000257492065},{"id":"https://openalex.org/keywords/cuckoo-hashing","display_name":"cuckoo hashing","score":0.8999999761581421},{"id":"https://openalex.org/keywords/high-dimensional-data","display_name":"high-dimensional data","score":0.8389999866485596},{"id":"https://openalex.org/keywords/image-retrieval","display_name":"image retrieval","score":0.7760000228881836}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8226172924041748},{"id":"https://openalex.org/C74270461","wikidata":"https://www.wikidata.org/wiki/Q1625299","display_name":"Locality-sensitive hashing","level":4,"score":0.8183379173278809},{"id":"https://openalex.org/C67388219","wikidata":"https://www.wikidata.org/wiki/Q207440","display_name":"Hash table","level":3,"score":0.7621759176254272},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.6064057946205139},{"id":"https://openalex.org/C99138194","wikidata":"https://www.wikidata.org/wiki/Q183427","display_name":"Hash function","level":2,"score":0.5436654686927795},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.5046676397323608},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.49640828371047974},{"id":"https://openalex.org/C33721204","wikidata":"https://www.wikidata.org/wiki/Q309949","display_name":"k-d tree","level":3,"score":0.4731356203556061},{"id":"https://openalex.org/C116738811","wikidata":"https://www.wikidata.org/wiki/Q608751","display_name":"Nearest neighbor search","level":2,"score":0.4714280962944031},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.44645392894744873},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.43707743287086487},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.39162927865982056},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.1788015067577362},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1427518129348755},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0993802547454834},{"id":"https://openalex.org/C140745168","wikidata":"https://www.wikidata.org/wiki/Q1210082","display_name":"Tree traversal","level":2,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/2093973.2094002","doi":"https://doi.org/10.1145/2093973.2094002","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2093973.2094002","pdf_url":null,"source":{"id":"https://openalex.org/S7407088447","display_name":"ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"},{"id":"pmh:oai:hub.hku.hk:10722/206262","doi":null,"is_oa":false,"landing_page_url":"http://hdl.handle.net/10722/206262","pdf_url":null,"source":{"id":"https://openalex.org/S4377196271","display_name":"The HKU Scholars Hub (University of Hong Kong)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I889458895","host_organization_name":"University of Hong Kong","host_organization_lineage":["https://openalex.org/I889458895"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference_Paper"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G6066569970","display_name":null,"funder_award_id":"9.17E+19","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8379971704","display_name":null,"funder_award_id":"W911NF-10-1-0506","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320338281","display_name":"Army Research Office","ror":"https://ror.org/05epdh915"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W100509257","https://openalex.org/W336063547","https://openalex.org/W1497953515","https://openalex.org/W1502916507","https://openalex.org/W1541459201","https://openalex.org/W1543003006","https://openalex.org/W1551730117","https://openalex.org/W1971238646","https://openalex.org/W1978339264","https://openalex.org/W1990111898","https://openalex.org/W2008967868","https://openalex.org/W2009498843","https://openalex.org/W2013355899","https://openalex.org/W2017086619","https://openalex.org/W2022651886","https://openalex.org/W2024794876","https://openalex.org/W2038276547","https://openalex.org/W2055839530","https://openalex.org/W2060029502","https://openalex.org/W2065281625","https://openalex.org/W2073880961","https://openalex.org/W2113210657","https://openalex.org/W2117961179","https://openalex.org/W2118123209","https://openalex.org/W2120490947","https://openalex.org/W2122817518","https://openalex.org/W2124592110","https://openalex.org/W2129017891","https://openalex.org/W2137101044","https://openalex.org/W2137706341","https://openalex.org/W2139241970","https://openalex.org/W2142040641","https://openalex.org/W2145065594","https://openalex.org/W2148781362","https://openalex.org/W2153226019","https://openalex.org/W2160831612","https://openalex.org/W2162006472","https://openalex.org/W2165888865","https://openalex.org/W2169336598","https://openalex.org/W2170037597","https://openalex.org/W2232710831","https://openalex.org/W2293597654","https://openalex.org/W2596585349","https://openalex.org/W2912601938","https://openalex.org/W2962923175","https://openalex.org/W2990138404","https://openalex.org/W3198160809","https://openalex.org/W4214596219","https://openalex.org/W4301953474","https://openalex.org/W6907659760"],"related_works":["https://openalex.org/W2073880961","https://openalex.org/W297502789","https://openalex.org/W2261553819","https://openalex.org/W2154567932","https://openalex.org/W3135737983","https://openalex.org/W6888828039","https://openalex.org/W3196483145","https://openalex.org/W4384943762","https://openalex.org/W6982107438","https://openalex.org/W4300910251"],"abstract_inverted_index":{"We":[0,17,115],"present":[1],"an":[2],"efficient":[3],"GPU-based":[4,98],"parallel":[5,43,86],"LSH":[6,21,69,148],"algorithm":[7,45],"to":[8,34,46,56,101],"perform":[9],"approximate":[10],"k-nearest":[11,26],"neighbor":[12],"computation":[13],"in":[14,112],"high-dimensional":[15],"spaces.":[16],"use":[18,41,97],"the":[19,37,42,67,93,109,117],"Bi-level":[20],"algorithm,":[22],"which":[23,105,126],"can":[24,138],"compute":[25],"neighbors":[27],"with":[28,123],"higher":[29],"accuracy":[30],"and":[31,74,89],"is":[32,83,106],"amenable":[33],"parallelization.":[35],"During":[36],"first":[38],"level,":[39],"we":[40,96],"RP-tree":[44],"partition":[47],"datasets":[48,122],"into":[49],"several":[50],"groups":[51],"so":[52],"that":[53],"items":[54],"similar":[55],"each":[57,72],"other":[58],"are":[59,127],"clustered":[60],"together.":[61],"The":[62,80],"second":[63],"level":[64],"involves":[65],"computing":[66],"Bi-Level":[68],"code":[70],"for":[71],"item":[73],"constructing":[75],"a":[76,145],"hierarchical":[77],"hash":[78,81],"table.":[79],"table":[82],"based":[84],"on":[85,119],"cuckoo":[87],"hashing":[88],"Morton":[90],"curves.":[91],"In":[92,133],"query":[94],"step,":[95],"work":[99],"queues":[100],"accelerate":[102],"short-list":[103],"search,":[104],"one":[107],"of":[108],"main":[110],"bottlenecks":[111],"LSH-based":[113],"algorithms.":[114],"demonstrate":[116],"results":[118],"large":[120],"image":[121],"200,000":[124],"images":[125],"represented":[128],"as":[129],"512":[130],"dimensional":[131],"vectors.":[132],"practice,":[134],"our":[135],"GPU":[136],"implementation":[137],"obtain":[139],"more":[140],"than":[141],"40X":[142],"acceleration":[143],"over":[144],"single-core":[146],"CPU-based":[147],"implementation.":[149]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":15},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":13},{"year":2017,"cited_by_count":7},{"year":2016,"cited_by_count":18},{"year":2015,"cited_by_count":14},{"year":2014,"cited_by_count":10},{"year":2013,"cited_by_count":7},{"year":2012,"cited_by_count":8}],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2016-06-24T00:00:00"}
