{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T22:29:30Z","timestamp":1766269770490,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":46,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,7,1]],"date-time":"2019-07-01T00:00:00Z","timestamp":1561939200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1849559, 1533823, 1745331"],"award-info":[{"award-number":["1849559, 1533823, 1745331"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002661","name":"Fonds De La Recherche Scientifique - FNRS","doi-asserted-by":"publisher","award":["MISU F 6001 1"],"award-info":[{"award-number":["MISU F 6001 1"]}],"id":[{"id":"10.13039\/501100002661","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,7]]},"DOI":"10.1145\/3329785.3329920","type":"proceedings-article","created":{"date-parts":[[2019,6,24]],"date-time":"2019-06-24T13:52:32Z","timestamp":1561384352000},"page":"1-9","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["GPU-Accelerated Similarity Self-Join for Multi-Dimensional Data"],"prefix":"10.1145","author":[{"given":"Michael","family":"Gowanlock","sequence":"first","affiliation":[{"name":"School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, U.S.A."}]},{"given":"Ben","family":"Karsin","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Universit\u00e9 libre de Bruxelles, Brussels, Belgium"}]}],"member":"320","published-online":{"date-parts":[[2019,7]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"http:\/\/images.nvidia.com\/content\/volta-architecture\/pdf\/volta-architecture-whitepaper.pdf. Accessed","author":"Volta Nvidia","year":"2018","unstructured":"{n.d.}. Nvidia Volta . http:\/\/images.nvidia.com\/content\/volta-architecture\/pdf\/volta-architecture-whitepaper.pdf. Accessed : Oct. 5, 2018 . {n.d.}. Nvidia Volta. http:\/\/images.nvidia.com\/content\/volta-architecture\/pdf\/volta-architecture-whitepaper.pdf. Accessed: Oct. 5, 2018."},{"volume-title":"https:\/\/www.top500.org\/lists\/2018\/06\/. Accessed","year":"2019","key":"e_1_3_2_1_2_1","unstructured":"{n.d.}. Top500. https:\/\/www.top500.org\/lists\/2018\/06\/. Accessed : Apr. 29, 2019 . {n.d.}. Top500. https:\/\/www.top500.org\/lists\/2018\/06\/. Accessed: Apr. 29, 2019."},{"key":"e_1_3_2_1_3_1","volume-title":"Efficient similarity search in sequence databases. Foundations of data organization and algorithms","author":"Agrawal Rakesh","year":"1993","unstructured":"Rakesh Agrawal , Christos Faloutsos , and Arun Swami . 1993. Efficient similarity search in sequence databases. Foundations of data organization and algorithms ( 1993 ), 69--84. Rakesh Agrawal, Christos Faloutsos, and Arun Swami. 1993. Efficient similarity search in sequence databases. Foundations of data organization and algorithms (1993), 69--84."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/FOCS.2006.49"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/304182.304187"},{"key":"e_1_3_2_1_6_1","volume-title":"Proc. of the Intl. Conf. on Very Large Data Bases. 918--929","author":"Arasu Arvind","year":"2006","unstructured":"Arvind Arasu , Venkatesh Ganti , and Raghav Kaushik . 2006 . Efficient Exact Set-similarity Joins . In Proc. of the Intl. Conf. on Very Large Data Bases. 918--929 . Arvind Arasu, Venkatesh Ganti, and Raghav Kaushik. 2006. Efficient Exact Set-similarity Joins. In Proc. of the Intl. Conf. on Very Large Data Bases. 918--929."},{"key":"e_1_3_2_1_7_1","unstructured":"P. Baldi P. Sadowski and D. Whiteson. 2014. Searching for exotic particles in high-energy physics with deep learning. Nature Communications 5 Article 4308 (July 2014). arXiv:hep-ph\/1402.4735  P. Baldi P. Sadowski and D. Whiteson. 2014. Searching for exotic particles in high-energy physics with deep learning. Nature Communications 5 Article 4308 (July 2014). arXiv:hep-ph\/1402.4735"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/1242572.1242591"},{"volume-title":"Adaptive control processes: a guided tour","author":"Bellman Richard E","key":"e_1_3_2_1_9_1","unstructured":"Richard E Bellman . 1961. Adaptive control processes: a guided tour . Princeton University press . Richard E Bellman. 1961. Adaptive control processes: a guided tour. Princeton University press."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/361002.361007"},{"key":"e_1_3_2_1_11_1","volume-title":"Proc. of the 12th Intl. Conf. on Music Information Retrieval.","author":"Bertin-Mahieux Thierry","year":"2011","unstructured":"Thierry Bertin-Mahieux , Daniel P.W. Ellis , Brian Whitman , and Paul Lamere . 2011 . The Million Song Dataset . In Proc. of the 12th Intl. Conf. on Music Information Retrieval. Thierry Bertin-Mahieux, Daniel P.W. Ellis, Brian Whitman, and Paul Lamere. 2011. The Million Song Dataset. In Proc. of the 12th Intl. Conf. on Music Information Retrieval."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/354756.354832"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/375663.375714"},{"volume-title":"Proc. 17th Intl. Conf. on Data Engineering. 411--420","author":"Bohm C.","key":"e_1_3_2_1_14_1","unstructured":"C. Bohm and H. P. Kriegel . 2001. A cost model and index architecture for the similarity join . In Proc. 17th Intl. Conf. on Data Engineering. 411--420 . C. Bohm and H. P. Kriegel. 2001. A cost model and index architecture for the similarity join. In Proc. 17th Intl. Conf. on Data Engineering. 411--420."},{"key":"e_1_3_2_1_15_1","unstructured":"Christian B\u00f6hm Robert Noll Claudia Plant and Andrew Zherdin. 2009. Index-supported Similarity Join on Graphics Processors. In BTW. 57--66.  Christian B\u00f6hm Robert Noll Claudia Plant and Andrew Zherdin. 2009. Index-supported Similarity Join on Graphics Processors. In BTW. 57--66."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2008.4497443"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2016.2631599"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.14778\/2732977.2732981"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/1327452.1327492"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/502512.502524"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jco.2009.02.011"},{"key":"e_1_3_2_1_22_1","volume-title":"Proc. of the 2nd KDD. 226--231","author":"Ester Martin","year":"1996","unstructured":"Martin Ester , Hans Kriegel , J\u00f6rg Sander , and Xiaowei Xu . 1996 . A density-based algorithm for discovering clusters in large spatial databases with noise . In Proc. of the 2nd KDD. 226--231 . Martin Ester, Hans Kriegel, J\u00f6rg Sander, and Xiaowei Xu. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proc. of the 2nd KDD. 226--231."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00288933"},{"volume-title":"2014 IEEE 30th Intl. Conf. on Data Engineering. 796--807","author":"Fries S.","key":"e_1_3_2_1_24_1","unstructured":"S. Fries , B. Boden , G. Stepien , and T. Seidl . 2014. PHiDJ: Parallel similarity self-join for high-dimensional vector data with MapReduce . In 2014 IEEE 30th Intl. Conf. on Data Engineering. 796--807 . S. Fries, B. Boden, G. Stepien, and T. Seidl. 2014. PHiDJ: Parallel similarity self-join for high-dimensional vector data with MapReduce. In 2014 IEEE 30th Intl. Conf. on Data Engineering. 796--807."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPSW.2019.00078"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2015.2500896"},{"volume-title":"GPU Accelerated Self-Join for the Distance Similarity Metric. In 2018 IEEE Intl. Parallel and Distributed Processing Symp. Workshops (IPDPSW). 477--486","author":"Gowanlock M.","key":"e_1_3_2_1_27_1","unstructured":"M. Gowanlock and B. Karsin . 2018 . GPU Accelerated Self-Join for the Distance Similarity Metric. In 2018 IEEE Intl. Parallel and Distributed Processing Symp. Workshops (IPDPSW). 477--486 . M. Gowanlock and B. Karsin. 2018. GPU Accelerated Self-Join for the Distance Similarity Metric. In 2018 IEEE Intl. Parallel and Distributed Processing Symp. Workshops (IPDPSW). 477--486."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS.2017.17"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/602259.602266"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btr172"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/1206049.1206056"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/1366102.1366104"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-012-0305-7"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2014.2347041"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpdc.2017.10.015"},{"volume-title":"Advances in spatial databases","author":"Koperski Krzysztof","key":"e_1_3_2_1_36_1","unstructured":"Krzysztof Koperski and Jiawei Han . 1995. Discovery of spatial association rules in geographic information databases . In Advances in spatial databases . Springer , 47--66. Krzysztof Koperski and Jiawei Han. 1995. Discovery of spatial association rules in geographic information databases. In Advances in spatial databases. Springer, 47--66."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.3881"},{"key":"e_1_3_2_1_38_1","unstructured":"M. Lichman. 2013. UCI Machine Learning Repository. http:\/\/archive.ics.uci.edu\/ml  M. Lichman. 2013. UCI Machine Learning Repository. http:\/\/archive.ics.uci.edu\/ml"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2008.4497520"},{"key":"e_1_3_2_1_40_1","unstructured":"NVIDIA. 2018. Pascal Tuning Guide. http:\/\/docs.nvidia.com\/cuda\/pascal-tuning-guide\/index.html. Accessed 18-June-2018.  NVIDIA. 2018. Pascal Tuning Guide. http:\/\/docs.nvidia.com\/cuda\/pascal-tuning-guide\/index.html. Accessed 18-June-2018."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPSW.2015.127"},{"key":"e_1_3_2_1_42_1","volume-title":"Workshop on power aware computing and system.","author":"Rofouei Mahsan","year":"2008","unstructured":"Mahsan Rofouei , Thanos Stathopoulos , Sebi Ryffel , William Kaiser , and Majid Sarrafzadeh . 2008 . Energy-aware high performance computing with graphic processing units . In Workshop on power aware computing and system. Mahsan Rofouei, Thanos Stathopoulos, Sebi Ryffel, William Kaiser, and Majid Sarrafzadeh. 2008. Energy-aware high performance computing with graphic processing units. In Workshop on power aware computing and system."},{"key":"e_1_3_2_1_43_1","first-page":"37","article-title":"MR-DSJ: Distance-Based Self-Join for Large-Scale Vector Data Analysis with MapReduce","volume":"214","author":"Seidl Thomas","year":"2013","unstructured":"Thomas Seidl , Sergej Fries , and Brigitte Boden . 2013 . MR-DSJ: Distance-Based Self-Join for Large-Scale Vector Data Analysis with MapReduce . In BTW , Vol. 214. 37 -- 56 . Thomas Seidl, Sergej Fries, and Brigitte Boden. 2013. MR-DSJ: Distance-Based Self-Join for Large-Scale Vector Data Analysis with MapReduce. In BTW, Vol. 214. 37--56.","journal-title":"BTW"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/MM.2011.65"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/SISAP.2009.9"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/2390226.2390229"}],"event":{"name":"SIGMOD\/PODS '19: International Conference on Management of Data","sponsor":["SIGMOD ACM Special Interest Group on Management of Data"],"location":"Amsterdam Netherlands","acronym":"SIGMOD\/PODS '19"},"container-title":["Proceedings of the 15th International Workshop on Data Management on New Hardware"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3329785.3329920","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3329785.3329920","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3329785.3329920","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:26:22Z","timestamp":1750206382000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3329785.3329920"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7]]},"references-count":46,"alternative-id":["10.1145\/3329785.3329920","10.1145\/3329785"],"URL":"https:\/\/doi.org\/10.1145\/3329785.3329920","relation":{},"subject":[],"published":{"date-parts":[[2019,7]]},"assertion":[{"value":"2019-07-01","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}