{"id":"https://openalex.org/W4381327384","doi":"https://doi.org/10.1145/3577193.3593738","title":"RT-kNNS Unbound: Using RT Cores to Accelerate Unrestricted Neighbor Search","display_name":"RT-kNNS Unbound: Using RT Cores to Accelerate Unrestricted Neighbor Search","publication_year":2023,"publication_date":"2023-06-20","ids":{"openalex":"https://openalex.org/W4381327384","doi":"https://doi.org/10.1145/3577193.3593738"},"language":"en","primary_location":{"id":"doi:10.1145/3577193.3593738","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3577193.3593738","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3577193.3593738","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 37th International Conference on Supercomputing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3577193.3593738","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5059294736","display_name":"Vani Nagarajan","orcid":"https://orcid.org/0009-0009-0416-5527"},"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":"Vani Nagarajan","raw_affiliation_strings":["Purdue University, West Lafayette, United States of America"],"raw_orcid":"https://orcid.org/0009-0009-0416-5527","affiliations":[{"raw_affiliation_string":"Purdue University, West Lafayette, United States of America","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092212158","display_name":"Durga Keerthi Mandarapu","orcid":"https://orcid.org/0009-0003-4984-0502"},"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":"Durga Mandarapu","raw_affiliation_strings":["Purdue University, West Lafayette, United States of America"],"raw_orcid":"https://orcid.org/0009-0003-4984-0502","affiliations":[{"raw_affiliation_string":"Purdue University, West Lafayette, United States of America","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075916086","display_name":"Milind Kulkarni","orcid":"https://orcid.org/0000-0001-6827-345X"},"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":"Milind Kulkarni","raw_affiliation_strings":["Purdue University, West Lafayette, United States of America"],"raw_orcid":"https://orcid.org/0000-0001-6827-345X","affiliations":[{"raw_affiliation_string":"Purdue University, West Lafayette, United States of America","institution_ids":["https://openalex.org/I219193219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I219193219"],"apc_list":null,"apc_paid":null,"fwci":15.438,"has_fulltext":true,"cited_by_count":20,"citation_normalized_percentile":{"value":0.98555936,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"289","last_page":"300"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9973000288009644,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.635412871837616},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.5444797277450562},{"id":"https://openalex.org/keywords/acceleration","display_name":"Acceleration","score":0.5116108059883118},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.5078490376472473},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.5059470534324646},{"id":"https://openalex.org/keywords/tracing","display_name":"Tracing","score":0.48551201820373535},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.46313005685806274},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.457253634929657},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.42988908290863037},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.42499104142189026},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2081545889377594},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.18694564700126648},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.1423218548297882},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.08442682027816772}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.635412871837616},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.5444797277450562},{"id":"https://openalex.org/C117896860","wikidata":"https://www.wikidata.org/wiki/Q11376","display_name":"Acceleration","level":2,"score":0.5116108059883118},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.5078490376472473},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.5059470534324646},{"id":"https://openalex.org/C138673069","wikidata":"https://www.wikidata.org/wiki/Q322229","display_name":"Tracing","level":2,"score":0.48551201820373535},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.46313005685806274},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.457253634929657},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.42988908290863037},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.42499104142189026},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2081545889377594},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.18694564700126648},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.1423218548297882},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.08442682027816772},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C74650414","wikidata":"https://www.wikidata.org/wiki/Q11397","display_name":"Classical mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3577193.3593738","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3577193.3593738","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3577193.3593738","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 37th International Conference on Supercomputing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3577193.3593738","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3577193.3593738","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3577193.3593738","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 37th International Conference on Supercomputing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2227270456","display_name":"SHF: Small: A Composable,  Sound Optimization Framework for Loops and Recursion","funder_award_id":"1908504","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5234923928","display_name":"Collaborative Research: PPoSS: LARGE: A Full-Stack Architecture for Sparse Computation","funder_award_id":"2216978","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8345018822","display_name":"SPX: Write Once, Run on Anything: Verified, Tuned Accelerator Kernels from High Level Specifications","funder_award_id":"1919197","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G93225853","display_name":null,"funder_award_id":"CCF-1919197","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4381327384.pdf","grobid_xml":"https://content.openalex.org/works/W4381327384.grobid-xml"},"referenced_works_count":23,"referenced_works":["https://openalex.org/W1848593906","https://openalex.org/W1967320885","https://openalex.org/W1988580225","https://openalex.org/W1991912219","https://openalex.org/W2041657594","https://openalex.org/W2081434183","https://openalex.org/W2115579991","https://openalex.org/W2122111042","https://openalex.org/W2145065594","https://openalex.org/W2165558283","https://openalex.org/W2460613287","https://openalex.org/W2479598871","https://openalex.org/W2494473000","https://openalex.org/W2952185982","https://openalex.org/W2991232928","https://openalex.org/W2998702515","https://openalex.org/W3003570628","https://openalex.org/W3111299119","https://openalex.org/W3113728200","https://openalex.org/W3128190052","https://openalex.org/W4220665239","https://openalex.org/W4238805501","https://openalex.org/W6945220697"],"related_works":["https://openalex.org/W2058965144","https://openalex.org/W2164382479","https://openalex.org/W98480971","https://openalex.org/W2150291671","https://openalex.org/W2027972911","https://openalex.org/W2146343568","https://openalex.org/W2013643406","https://openalex.org/W2157978810","https://openalex.org/W2597809628","https://openalex.org/W3046370962"],"abstract_inverted_index":{"The":[0],"problem":[1,90],"of":[2,8,84,168],"identifying":[3],"the":[4,81,95,115,135,150],"k-Nearest":[5],"Neighbors":[6],"(kNNS)":[7],"a":[9,19,24,87,92,119,122],"point":[10,40],"has":[11,44,56],"proven":[12],"to":[13,50,66,74,86,101,109,117,179],"be":[14,177],"very":[15],"useful":[16],"both":[17],"as":[18,23,36],"standalone":[20],"application":[21],"and":[22,39,124,174],"subroutine":[25],"in":[26,33,63],"larger":[27],"applications.":[28],"Given":[29],"its":[30],"far-reaching":[31],"applicability":[32],"areas":[34],"such":[35],"machine":[37],"learning":[38],"clouds,":[41],"extensive":[42],"research":[43],"gone":[45],"into":[46],"leveraging":[47],"GPU":[48],"acceleration":[49,76],"solve":[51],"this":[52,130],"problem.":[53],"Recent":[54],"work":[55],"shown":[57],"that":[58,163],"using":[59,77],"Ray":[60],"Tracing":[61],"cores":[62,108],"recent":[64],"GPUs":[65],"accelerate":[67,110,180],"kNNS":[68,85],"is":[69,166],"much":[70],"more":[71],"efficient":[72],"compared":[73],"traditional":[75],"shader":[78],"cores.":[79],"However,":[80],"existing":[82,172],"translation":[83],"ray":[88],"tracing":[89],"imposes":[91],"constraint":[93],"on":[94],"search":[96,120,151],"space":[97,152],"for":[98],"neighbors.":[99,128,160],"Due":[100],"this,":[102],"we":[103,132,147],"can":[104,126,175],"only":[105],"use":[106],"RT":[107],"fixed-radius":[111,181],"kNNS,":[112],"which":[113],"requires":[114],"user":[116],"set":[118],"radius":[121],"priori":[123],"hence":[125],"miss":[127],"In":[129],"work,":[131],"propose":[133],"TrueKNN,":[134],"first":[136],"unbounded":[137],"RT-accelerated":[138],"neighbor":[139,182],"search.":[140],"TrueKNN":[141],"adopts":[142],"an":[143],"iterative":[144],"approach":[145,165],"where":[146],"incrementally":[148],"grow":[149],"until":[153],"all":[154],"points":[155],"have":[156],"found":[157],"their":[158],"k":[159],"We":[161],"show":[162],"our":[164],"orders":[167],"magnitude":[169],"faster":[170],"than":[171],"approaches":[173],"even":[176],"used":[178],"searches.":[183]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":7}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
