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Abstract

GPGPU programming requires adjusting existing algorithms and often inventing new ones in order to achieve maximum performance. Solutions already created for supercomputers in nineties are not applicable since SIMD GPU devices are in many aspects different than vector supercomputers. This paper presents a new implementation of B + -tree index for GPU processors. It may be used in cases when processing parallelism and order of elements are equally important in computation. The solution is based on data representation optimal for GPU processing and an efficient parallel tree creation algorithm. We also deeply compare GPU B + -tree and other solutions.

Keywords

Binary Search Thread Block Memory Latency Memory Copying Brute Force Search 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Krzysztof Kaczmarski
    • 1
  1. 1.Warsaw University of TechnologyWarsawPoland

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