Hardware acceleration in commercial databases: a case study of spatial operations

  • Authors:

  • Nagender Bandi;Chengyu Sun;Divyakant Agrawal;Amr El Abbadi

  • Affiliations:

  • University of California, Santa Barbara;California State University, Los Angeles;University of California, Santa Barbara;University of California, Santa Barbara

  • Venue:
  • VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
  • Year:
  • 2004

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Abstract

Traditional databases have focused on the issue of reducing I/O cost as it is the bottleneck in many operations. As databases become increasingly accepted in areas such as Geographic Information Systems (GIS) and Bioinformatics, commercial DBMS need to support data types for complex data such as spatial geometries and protein structures. These non-conventional data types and their associated operations present new challenges. In particular, the computational cost of some spatial operations can be orders of magnitude higher than the I/O cost. In order to improve the performance of spatial query processing, innovative solutions for reducing this computational cost are beginning to emerge. Recently, it has been proposed that hard-ware acceleration of an off-the-shelf graphics card can be used to reduce the computational cost of spatial operations. However, this proposal is preliminary in that it establishes the feasibility of the hardware assisted approach in a stand-alone setting but not in a real-world commercial database. In this paper we present an architecture to show how hardware acceleration of an off-the-shelf graphics card can be integrated into a popular commercial database to speed up spatial queries. Extensive experimentation with real-world datasets shows that significant improvement in the performance of spatial operations can be achieved with this integration. The viability of this approach underscores the significance of a tighter integration of hardware acceleration into commercial databases for spatial applications.