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Scaling database performance on GPUs

Author

Listed:
  • Yue-Shan Chang

    (National Taipei University)

  • Ruey-Kai Sheu

    (Tunghai University)

  • Shyan-Ming Yuan

    (Providence University)

  • Jyn-Jie Hsu

    (National Chiao Tung University)

Abstract

The market leaders of Cloud Computing try to leverage the parallel-processing capability of GPUs to provide more economic services than traditions. As the cornerstone of enterprise applications, database systems are of the highest priority to be improved for the performance and design complexity reduction. It is the purpose of this paper to design an in-memory database, called CUDADB, to scale up the performance of the database system on GPU with CUDA. The details of implementation and algorithms are presented, and the experiences of GPU-enabled CUDA database operations are also shared in this paper. For performance evaluation purposes, SQLite is used as the comparison target. From the experimental results, CUDADB performs better than SQLite for most test cases. And, surprisingly, the CUDADB performance is independent from the number of data records in a query result set. The CUDADB performance is a static proportion of the total number of data records in the target table. Finally, this paper comes out a concept of turning point that represents the difference ratio between CUDADB and SQLite.

Suggested Citation

  • Yue-Shan Chang & Ruey-Kai Sheu & Shyan-Ming Yuan & Jyn-Jie Hsu, 2012. "Scaling database performance on GPUs," Information Systems Frontiers, Springer, vol. 14(4), pages 909-924, September.
    • Handle: RePEc:spr:infosf:v:14:y:2012:i:4:d:10.1007_s10796-011-9322-0
    DOI: 10.1007/s10796-011-9322-0
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    Citations

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    Cited by:

    1. Chidchanok Choksuchat & Chantana Chantrapornchai, 0. "Practical parallel string matching framework for RDF entailments with GPUs," Information Systems Frontiers, Springer, vol. 0, pages 1-20.
    2. Chulhwan Chris Bang, 2015. "Information systems frontiers: Keyword analysis and classification," Information Systems Frontiers, Springer, vol. 17(1), pages 217-237, February.
    3. Jason J. Jung & Yue-Shan Chang & Ying Liu & Chao-Chin Wu, 2012. "Advances in intelligent grid and cloud computing," Information Systems Frontiers, Springer, vol. 14(4), pages 823-825, September.
    4. Chidchanok Choksuchat & Chantana Chantrapornchai, 2018. "Practical parallel string matching framework for RDF entailments with GPUs," Information Systems Frontiers, Springer, vol. 20(4), pages 863-882, August.

    More about this item

    Keywords

    GPU; CUDA; SQLite; In-Memory Database;
    All these keywords.

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