{"id":162664,"date":"2012-04-16T00:00:00","date_gmt":"2012-04-16T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/gpu-based-minwise-hashing\/"},"modified":"2018-10-16T20:42:51","modified_gmt":"2018-10-17T03:42:51","slug":"gpu-based-minwise-hashing","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/gpu-based-minwise-hashing\/","title":{"rendered":"GPU-Based Minwise Hashing"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Minwise hashing is a standard technique for efficient set similarity estimation in the context of search. The recent work of b-bit minwise hashing provided a substantial improvement by storing only the lowest b bits of each hashed value. Both minwise hashing and b-bit minwise hashing require an expensive preprocessing step for applying k (e.g., k = 500) permutations on the entire data in order to compute k minimal values as the hashed data. In this paper, we developed a parallelization scheme using GPUs, which reduced the processing time by a factor of 20-80x. Reducing the preprocessing time is highly beneficial in practice, for example, for duplicate web page detection (where minwise hashing is a major step in the crawling pipeline) or for increasing the testing speed of online classifiers (when the test data are not preprocessed).<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Minwise hashing is a standard technique for efficient set similarity estimation in the context of search. The recent work of b-bit minwise hashing provided a substantial improvement by storing only the lowest b bits of each hashed value. Both minwise hashing and b-bit minwise hashing require an expensive preprocessing step for applying k (e.g., k [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13555],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-162664","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-search-information-retrieval","msr-locale-en_us"],"msr_publishername":"Association for Computing Machinery, Inc.","msr_edition":"21st International World Wide Web 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