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Many big data processing applications rely on a \emph{top-k retrieval} building block, which selects (or approximates) the $k$ highest-scoring data items based on an aggregation of features. In web search, for instance, a document’s score is the sum of its scores for all query terms. Top-k retrieval is often used to sift through massive data and identify a smaller subset of it for further analysis. Because it filters out the bulk of the data, it often constitutes the main performance bottleneck.

Beyond the rise in data sizes, today’s data processing scenarios also increase the number of features contributing to the overall score. In web search, for example, verbose queries are becoming mainstream, while state-of-the-art algorithms fail to process long queries in real-time.

We present Sparta, a practical parallel algorithm that exploits multi-core hardware for fast (approximate) top-k retrieval. Thanks to lightweight coordination and judicious context sharing among threads, Sparta scales both in the number of features and in the searched index size. In our web search case study on 50M documents, Sparta processes $12$-term queries more than twice as fast as the state-of-the-art. On a tenfold bigger index, Sparta processes queries at the same speed, whereas the average latency of existing algorithms soars to be an order-of-magnitude larger than Sparta’s.

Mon 24 Feb
Times are displayed in time zone: (GMT-07:00) Tijuana, Baja California change

10:55 - 12:35: Main Conference - Machine Learning/Big Data (Mediterranean Ballroom)
Chair(s): Shuaiwen Leon SongUniversity of Sydney
PPoPP-2020-papers10:55 - 11:20
Da YanHong Kong University of Science and Technology, Wei WangHong Kong University of Science and Technology, Xiaowen ChuHong Kong Baptist University
PPoPP-2020-papers11:20 - 11:45
Shigang LiDepartment of Computer Science, ETH Zurich, Tal Ben-NunDepartment of Computer Science, ETH Zurich, Salvatore Di GirolamoDepartment of Computer Science, ETH Zurich, Dan AlistarhIST Austria, Torsten HoeflerDepartment of Computer Science, ETH Zurich
PPoPP-2020-papers11:45 - 12:10
Gali SheffiTechnion - Israel, Dmitry BasinYahoo Research, Edward BortnikovYahoo Research, David CarmelAmazon, Idit KeidarTechnion - Israel institute of technology
PPoPP-2020-papers12:10 - 12:35
Jiannan TianUniversity of Alabama, Sheng DiArgonne National Laboratory, Chengming ZhangUniversity of Alabama, Xin Liang, Sian JinUniversity of Alabama, Dazhao ChengUniversity of North Carolina at Charlotte, Dingwen TaoUniversity of Alabama, Franck CappelloArgonne National Laboratory