Abstract
Onedimensional or multidimensional range query is one of the most important query of physical implementation of DBMS. The number of compared items (of a data structure) can be enormous especially for lower selectivity of the range query. The number of compare operations increases for more complex items (or tuples) with the longer length, e.g. words stored in a B-tree. Due to the possibly high number of compare operations executed during the range query processing, we can take into account hardware devices providing a parallel task computation like CPU’s SIMD or GPU. In this paper, we show the performance and scalability of sequential, index, CPU’s SIMD, and GPU variants of the range query algorithm. These results make possible a future integration of these computation devices into a DBMS kernel.
Work is partially supported by Grant of GACR No. GAP202/10/0573.
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References
Bayer, R., McCreight, E.: Organization and Maintenance of Large Ordered Indexes. Acta Informatica 3(1), 173–189 (1972)
Beckmann, N., Kriegel, H.P., Schneider, R., Seeger, B.: The R*-Tree: An Efficient and Robust Access Method for Points and Rectangles. In: Proceedings of the ACM International Conference on Management of Data, SIGMOD 1990 (1990)
Beier, F., Kilias, T., Sattler, K.U.: GiST Scan Acceleration using Coprocessors. In: Proceedings of 8th Int. Workshop on Data Management on New Hardware, DaMoN 2012 (2012)
Chhugani, J., Nguyen, A.D., Lee, V.W., Macy, W., Hagog, M., Chen, Y.K., Baransi, A., Kumar, S., Dubey, P.: Efficient Implementation of Sorting on Multi-Core SIMD CPU Architecture. Proceedings of the VLDB Endowment 1(2) (2008)
Chovanec, P., Krátký, M.: Processing of Multidimensional Range Query Using SIMD Instructions. In: Abd Manaf, A., Sahibuddin, S., Ahmad, R., Mohd Daud, S., El-Qawasmeh, E. (eds.) ICIEIS 2011, Part IV. CCIS, vol. 254, pp. 223–237. Springer, Heidelberg (2011)
Chovanec, P., Krátký, M., Bača, R.: Optimization of Disk Accesses for Multidimensional Range Queries. In: Bringas, P.G., Hameurlain, A., Quirchmayr, G. (eds.) DEXA 2010. LNCS, vol. 6261, pp. 358–367. Springer, Heidelberg (2010)
Farber, R.: CUDA Application Design and Development, 1st edn. Morgan Kaufmann (2011)
Freeston, M.: A General Solution of the n-dimensional B-tree Problem. In: Proceedings of the ACM International Conference on Management of Data, SIGMOD 1995. ACM Press (1995)
Garcia, V., Debreuve, E., Barlaud, M.: Fast k Nearest Neighbor Search using GPU. In: Computer Vision and Pattern Recognition Workshops, pp. 1–6. IEEE Computer Society (2008)
Guttman, A.: R-Trees: A Dynamic Index Structure for Spatial Searching. In: Proceedings of the ACM International Conference on Management of Data (SIGMOD 1984), pp. 47–57. ACM Press (June 1984)
Hennessy, J.L., Patterson, D.A.: Computer Architecture: A Quantitative Approach, 4th edn. Morgan Kaufmann (2006)
Khronos: Khronos: Opencl (2012), http://www.khronos.org/opencl/
Kirk, D.B., Mei, W., Hwu, W.: Programming Massively Parallel Processors: A Hands-on Approach. Applications of GPU Computing Series. Morgan Kaufmann (2010)
Krátký, M., Pokorný, J., Snášel, V.: Implementation of XPath Axes in the Multi-dimensional Approach to Indexing XML Data. In: Lindner, W., Fischer, F., Türker, C., Tzitzikas, Y., Vakali, A.I. (eds.) EDBT 2004. LNCS, vol. 3268, pp. 219–229. Springer, Heidelberg (2004)
Lahdenmäki, T., Leach, M.: Relational Database Index Design and the Optimizers. John Wiley and Sons, New Jersey (2005)
Lightstone, S.S., Teorey, T.J., Nadeau, T.: Physical Database Design: the Database Professional’s Guide. Morgan Kaufmann (2007)
nVIDIA: Cuda Programming Guide (2012), http://developer.download.nvidia.com/compute/DevZone/docs/html/C/doc/CUDA_C_Programming_Guide.pdf
nVIDIA: nVIDIA Fermi - White Paper (2012), http://www.nvidia.com/content/fermi_white_papers/NVIDIA_Fermi_Compute_Architecture_Whitepaper.pdf
Samet, H.: Foundations of Multidimensional and Metric Data Structures. Morgan Kaufmann (2006)
Servetti, A., Rinotti, A., De Martin, J.: Fast Implementation of the MPEG-4 AAC Main and Low Complexity Decoder. In: Proceedings of Acoustics, Speech, and Signal Processing, ICASSP 2004 (2004)
Shahbahrami, A., Juurlink, B., Vassiliadis, S.: Performance Comparison of SIMD Implementations of the Discrete Wavelet Transform. In: Proceedings of Application-Specific Systems, Architecture Processors, ASAP 2005 (2005)
Slingerland, N., Smith, A.J.: Multimedia Extensions for General Purpose Microprocessors: A Survey. Technical report CSD-00-1124, University of California at Berkeley (2000)
Stonebraker, M., Abadi, D., Batkin, A., Chen, X., Cherniack, M., Ferreira, M., Lau, E., Lin, A., Madden, S.: C-store: A Column Oriented DBMS. In: Proceedings of the International Conference on Very Large Data Bases, VLDB 2005 (2005)
Willhalm, T., Popovici, N., Boshmaf, Y., Plattner, H., Zeier, A., Schaffner, J.: SIMD-Scan: Ultra Fast In-Memory Table Scan Using On-Chip Vector Processing Units. Proceedings of the VLDB Endowment 2(1) (2009)
Zhou, J., Ross, K.A.: Implementing Database Operations Using SIMD Instructions. In: Proceedings of the ACM International Conference on Management of Data, SIGMOD 2002 (2002)
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Bednář, P., Gajdoš, P., Krátký, M., Chovanec, P. (2013). Processing of Range Query Using SIMD and GPU. In: Pechenizkiy, M., Wojciechowski, M. (eds) New Trends in Databases and Information Systems. Advances in Intelligent Systems and Computing, vol 185. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32518-2_2
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DOI: https://doi.org/10.1007/978-3-642-32518-2_2
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