• DocumentCode
    1667795
  • Title

    Matrix-Based XML Stream Processing Using a GPU

  • Author

    Soo-Hyung Kim ; Yoon-Joon Lee ; Lee, John Jaehwan

  • Author_Institution
    Sch. of Comput., Korea Adv. Inst. of Sci. & Technol., Daejeon, South Korea
  • fYear
    2015
  • Firstpage
    694
  • Lastpage
    697
  • Abstract
    With the advent of GPGPU computing, a major paradigm shift in parallel computation in recent years, massive parallel processing, has become available at a relatively low cost. However, conventional XML stream processing algorithms do not utilize the advantages of GPUs since the algorithms were intended for single-thread execution. In this research, we propose a GPU-accelerated, matrix-based XML stream processing methodology in which a large collection of XPath queries are transformed into matrixes of binary values and the XML stream is also converted to two matrix indexes. Then, the processing of a number of queries is transformed to simple bit-AND Boolean operations. With the XMark benchmark data set, we show that the proposed algorithm outperforms the conventional algorithms by about eight times.
  • Keywords
    Boolean functions; XML; graphics processing units; matrix algebra; parallel processing; query processing; GPGPU computing; XMark benchmark data set; XPath queries; bit-AND Boolean operations; massive parallel processing; matrix indexes; matrix-based XML stream processing; query processing; single-thread execution; Filtering; Graphics processing units; Indexes; Matrix converters; Matrix decomposition; Parallel processing; XML; GPGPU; Parallel Processing; XML Stream;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Big Data (BigData Congress), 2015 IEEE International Congress on
  • Conference_Location
    New York, NY
  • Print_ISBN
    978-1-4673-7277-0
  • Type

    conf

  • DOI
    10.1109/BigDataCongress.2015.111
  • Filename
    7207295