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Code Optim."],"published-print":{"date-parts":[[2018,12,31]]},"abstract":"<jats:p>\n            We introduce P\n            <jats:sc>oker<\/jats:sc>\n            , a permutation-based approach for vectorizing multiple queries over B\n            <jats:sup>+<\/jats:sup>\n            -trees. Our key insight is to combine vector loads and path-encoding-based permutations to alleviate memory latency while keeping the number of key comparisons needed for a query to a minimum. Implemented as a C++ template library, P\n            <jats:sc>oker<\/jats:sc>\n            represents a general-purpose solution for vectorizing the queries over indexing trees on multi-core processors equipped with SIMD units. For a set of five representative benchmarks evaluated with 24 configurations each, P\n            <jats:sc>oker<\/jats:sc>\n            outperforms the state of the art by 2.11x with one single thread and 2.28x with eight threads on an Intel Broadwell processor that supports 256-bit AVX2, on average. In addition, strip-mining queries will further improve P\n            <jats:sc>oker<\/jats:sc>\n            \u2019s performance by 1.21x (with one single thread) and 1.31x (with eight threads), on average.\n          <\/jats:p>","DOI":"10.1145\/3280850","type":"journal-article","created":{"date-parts":[[2018,11,16]],"date-time":"2018-11-16T13:08:54Z","timestamp":1542373734000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["P\n            <scp>oker<\/scp>"],"prefix":"10.1145","volume":"15","author":[{"given":"Feng","family":"Zhang","sequence":"first","affiliation":[{"name":"UNSW Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingling","family":"Xue","sequence":"additional","affiliation":[{"name":"UNSW Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2018,11,16]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"1997. 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