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Increasing the Efficiency of GPU Bitmap Index Query Processing

Part of the Lecture Notes in Computer Science book series (LNISA,volume 12114)


Once exotic, computational accelerators are now commonly available in many computing systems. Graphics processing units (GPUs) are perhaps the most frequently encountered computational accelerators. Recent work has shown that GPUs are beneficial when analyzing massive data sets. Specifically related to this study, it has been demonstrated that GPUs can significantly reduce the query processing time of database bitmap index queries. Bitmap indices are typically used for large, read-only data sets and are often compressed using some form of hybrid run-length compression.

In this paper, we present three GPU algorithm enhancement strategies for executing queries of bitmap indices compressed using Word Aligned Hybrid compression: 1) data structure reuse 2) metadata creation with various type alignment and 3) a preallocated memory pool. The data structure reuse greatly reduces the number of costly memory system calls. The use of metadata exploits the immutable nature of bitmaps to pre-calculate and store necessary intermediate processing results. This metadata reduces the number of required query-time processing steps. Preallocating a memory pool can reduce or entirely remove the overhead of memory operations during query processing. Our empirical study showed that performing a combination of these strategies can achieve 33\(\times \) to 113\(\times \) speedup over the unenhanced implementation.


  • Bitmap indices
  • Big data
  • Query processing
  • GPU

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  • DOI: 10.1007/978-3-030-59419-0_21
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  1. Andrzejewski, W., Wrembel, R.: GPU-WAH: applying GPUs to compressing bitmap indexes with word aligned hybrid. In: Bringas, P.G., Hameurlain, A., Quirchmayr, G. (eds.) DEXA 2010. LNCS, vol. 6262, pp. 315–329. Springer, Heidelberg (2010).

    CrossRef  Google Scholar 

  2. Andrzejewski, W., Wrembel, R.: GPU-PLWAH: GPU-based implementation of the PLWAH algorithm for compressing bitmaps. Control Cybern. 40, 627–650 (2011)

    MATH  Google Scholar 

  3. Antoshenkov, G.: Byte-aligned bitmap compression. In: Proceedings DCC 1995 Data Compression Conference, p. 476. IEEE (1995)

    Google Scholar 

  4. Bonneville power administration.

  5. Bradlow, E., Gangwar, M., Kopalle, P., Voleti, S.: The role of big data and predictive analytics in retailing. J. Retail. 93, 79–95 (2017)

    CrossRef  Google Scholar 

  6. Chambi, S., Lemire, D., Kaser, O., Godin, R.: Better bitmap performance with roaring bitmaps. Softw. Pract. Exp. 46(5), 709–719 (2016)

    CrossRef  Google Scholar 

  7. Colantonio, A., Di Pietro, R.: CONCISE: compressed ‘n’ composable integer set. Inf. Process. Lett. 110(16), 644–650 (2010)

    CrossRef  Google Scholar 

  8. Corrales, F., Chiu, D., Sawin, J.: Variable length compression for bitmap indices. In: Hameurlain, A., Liddle, S.W., Schewe, K.-D., Zhou, X. (eds.) DEXA 2011. LNCS, vol. 6861, pp. 381–395. Springer, Heidelberg (2011).

    CrossRef  Google Scholar 

  9. Davenport, T., Dyche, J.: Big data in big companies. Technical report, International Institute for Analytics (2013)

    Google Scholar 

  10. Deliège, F., Pedersen, T.B.: Position list word aligned hybrid: optimizing space and performance for compressed bitmaps. In: International Conference on Extending Database Technology, EDBT 2010, pp. 228–239 (2010)

    Google Scholar 

  11. Erevelles, S., Fukawa, N., Swaynea, L.: Big data consumer analytics and the transformation of marketing. J. Bus. Res. 69, 897–904 (2016)

    CrossRef  Google Scholar 

  12. Fusco, F., Stoecklin, M.P., Vlachos, M.: NET-FLi: on-the-fly compression, archiving and indexing of streaming network traffic. VLDB 3(2), 1382–1393 (2010)

    Google Scholar 

  13. Guzun, G., Canahuate, G., Chiu, D., Sawin, J.: A tunable compression framework for bitmap indices. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 484–495. IEEE (2014)

    Google Scholar 

  14. Hou, Q., Sun, X., Zhou, K., Lauterbach, C., Manocha, D.: Memory-scalable GPU spatial hierarchy construction. IEEE Trans. Vis. Comput. Graph. 17(4), 466–474 (2011)

    CrossRef  Google Scholar 

  15. Lichman, M.: UCI machine learning repository (2013). url

    Google Scholar 

  16. Lloyd, S.: Least squares quantization in PCM. IEEE Trans. Inf. Theor. 28(2), 129–137 (1982)

    CrossRef  MathSciNet  Google Scholar 

  17. Marr, B.: Starbucks: using big data, analytics and artificial intelligence to boost performance. Forbes, May 2018.

  18. McAfee, A., Brynjolfsson, E.: Big data: the management revolution. Harvard Bus. Rev. 90, 61–68 (2012)

    Google Scholar 

  19. Nelson, M., Sorenson, Z., Myre, J., Sawin, J., Chiu, D.: GPU acceleration of range queries over large data sets. In: Proceedings of the 6th IEEE/ACM International Conference on Big Data Computing, Application, and Technologies (BDCAT 2019), pp. 11–20 (2019)

    Google Scholar 

  20. Sariyar, M., Borg, A., Pommerening, K.: Controlling false match rates in record linkage using extreme value theory. J. Biomed. Inf. 44(4), 648–654 (2011)

    CrossRef  Google Scholar 

  21. Wang, L., et al.: SuperNeurons: dynamic GPU memory management for training deep neural networks. In: Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, pp. 41–53 (2018)

    Google Scholar 

  22. Wu, K., Otoo, E.J., Shoshani, A., Nordberg, H.: Notes on design and implementation of compressed bit vectors. Technical report, LBNL/PUB-3161, Lawrence Berkeley National Laboratory (2001)

    Google Scholar 

  23. Wu, K., Otoo, E.J., Shoshani, A.: Compressing bitmap indexes for faster search operations. In: Proceedings 14th International Conference on Scientific and Statistical Database Management, pp. 99–108. IEEE (2002)

    Google Scholar 

  24. Wu, K., Otoo, E.J., Shoshani, A.: Optimizing bitmap indices with efficient compression. ACM Trans. Database Syst. 31(1), 1–38 (2006)

    CrossRef  Google Scholar 

  25. You, S., Zhang, J., Gruenwald, L.: Parallel spatial query processing on GPUs using r-trees. In: Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, pp. 23–31 (2013)

    Google Scholar 

  26. Zaker, M., Phon-Amnuaisuk, S., Haw, S.C.: An adequate design for large data warehouse systems: bitmap index versus b-tree index. Int. J. Comput. Commun. 2, 39–46 (2008)

    Google Scholar 

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Tran, B., Schaffner, B., Sawin, J., Myre, J.M., Chiu, D. (2020). Increasing the Efficiency of GPU Bitmap Index Query Processing. In: Nah, Y., Cui, B., Lee, SW., Yu, J.X., Moon, YS., Whang, S.E. (eds) Database Systems for Advanced Applications. DASFAA 2020. Lecture Notes in Computer Science(), vol 12114. Springer, Cham.

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