Efficient parallel lists intersection and index compression algorithms using graphics processing units

Naiyong Ao, Fan Zhang, Di Wu, Douglas Stones, Gang Wang, Xiaoguang Liu, Jing Liu, Sheng Lin

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

49 Citations (Scopus)


Major web search engines answer thousands of queries per second requesting information about billions of web pages. The data sizes and query loads are growing at an exponential rate. To manage the heavy workload, we consider techniques for utilizing a Graphics Processing Unit (GPU). We investigate new approaches to improve two important operations of search engines lists intersection and index compression. For lists intersection, we develop techniques for efficient implementation of the binary search algorithm for parallel computation. We inspect some representative real-world datasets and and that a succiently long inverted list has an overall linear rate of increase. Based on this observation, we propose Linear Regression and Hash Segmentation techniques for contracting the search range. For index compression, the traditional d-gap based compression schemata are not well-suited for parallel computation, so we propose a Linear Regression Compression schema which has an inherent parallel structure. We further discuss how to efficiently intersect the compressed lists on a GPU. Our experimental results show signifcant improvements in the query processing throughput on several datasets.
Original languageEnglish
Title of host publicationProceedings of the VLDB Endowment
EditorsH V Jagadish, Jose Blakeley, Joseph M Hellerstein, Nick Koudas, Wolfgang Lehner, Sunita Sarawagi, Uwe Rohm
Place of PublicationUSA
PublisherAssociation for Computing Machinery (ACM)
Pages470 - 481
Number of pages12
Publication statusPublished - 2011
EventInternational Conference on Very Large Databases 2011 - The Westin, Seattle, United States of America
Duration: 29 Aug 20113 Sep 2011
Conference number: 37th


ConferenceInternational Conference on Very Large Databases 2011
Abbreviated titleVLDB 2011
CountryUnited States of America

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