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A Parallel Bulk Loading Method for $B^+$-Tree Using CUDA  

Sung, Joo-Ho (명지대학교 컴퓨터공학과)
Lee, Yoon-Woo (명지대학교 컴퓨터공학과)
Han, A (명지대학교 컴퓨터공학과)
Choi, Won-Ik (인하대학교 정보통신공학과)
Kwon, Dong-Seop (명지대학교 컴퓨터공학과)
Publication Information
Journal of KIISE:Computing Practices and Letters / v.16, no.6, 2010 , pp. 707-711 More about this Journal
Abstract
Most relational database systems provide $B^+$-trees as their main index structures, and use bulk-loading techniques for creating new $B^+$-trees on existing data from scratch. Although bulk loadings are more effective than inserting keys one by one, they are still time-consuming because they have to sort all the keys from large data. To improve the performance of bulk loadings, this paper proposes an efficient parallel bulk loading method for $B^+$-trees based on CUDA, which is a parallel computing architecture developed by NVIDIA to utilize computing powers of graphic processor units for general purpose computing. Experimental results show that the proposed method enhance the performance more than 70 percents compared to existing bulk loading methods.
Keywords
GPGPU; CUDA; $B^+$-tree; bulk loading;
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  • Reference
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