Abstract
Data is growing at faster speed. In cloud, fast processing has become essential need to process hefty data. The earlier EGENMR system was developed for process large amount of data present in cloud repositories with the help of MapReduce functions we concluded that the system is better than the latest techniques for processing large amount of data. In this paper, we are enhancing EGENMR by further enhancing the speed of database query operation by using GPU as a co-processor. A theoretical comparison is made in terms of time taken and complexity for hybrid query processing using GPU and EGENMR.
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References
S. Malhotra, M.N. Doja, B. Alam and M. Alam, “E-GENMR: Enhanced Generalized Query Processing using Double hashing technique through Map Reduce in cloud Database Management System,” Journal of Computer Science, 2017.
J. Dean and S. Ghemawat, “MapReduce: simplified data processing on large clusters,” Communications of the ACM, vol. 51, no. 1, pp. 107–113, 2008.
P. Ghodsnia, “An In-GPU-Memory Column-Oriented Database for Processing Analytical Workloads,” in In proceedings of VLDB PhD Workshop, 2012.
B. He, K. Yang, R. Fang, M. Lu, N. Govindaraju, Q. Luo and P. Sander, “Relational joins on graphics processors,” Proceedings of the ACM SIGMOD international conference on Management of data, pp. 511–524, 2008.
N. K. Govindaraju, J. Gray, R. Kumar and D. Manocha, “GPUTeraSort: high performance graphics co-processor sorting for large database management,” Proceedings of the 2006 ACM SIGMOD international conference on Management of data, pp. 325–336, 2006.
J. Cheng, M. Grossman and T. McKercher, Professional CUDA C Programming, Indianapolis, Indiana: John Wiley & Sons, 2014.
P. Bakkum and K. Skadron, “Accelerating SQL database operations on a GPU with CUDA,” in Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units, Pittsburgh, Pennsylvania, 2010.
S. Breß, E. Schallehn and I. Geist, “Towards Optimization of Hybrid CPU/GPU Query Plans in Database Systems,” New Trends in Databases and Information Systems, vol. 185, pp. 27–35, 2013.
S. Breß and G. Saake, “Why it is time for a HyPE: A hybrid query processing engine for efficient GPU coprocessing in DBMS,” in Proceedings of the VLDB Endowment, Trento, Italy, 2013.
S. Breß, N. Siegmund, M. Heimel, M. Saecker, T. Lauer, L. Bellatreche and G. Saake, “Load-aware inter-co-processor parallelism in database query processing,” Data & Knowledge Engineering, vol. 93, no. C, pp. 60–79, 2014.
K. Angstadt and E. Harcourt, “A virtual machine model for accelerating relational database joins using a general purpose GPU,” in Proceedings of the Symposium on High Performance Computing, Alexandria, Virginia, 2015.
B. He, M. Lu, K. Yang, R. Fang, N. K. Govindaraju, Q. Luo and P. V. Sander, “Relational query coprocessing on graphics processors,” ACM Transactions on Database Systems, vol. 34, no. 4, pp. 1–39, 2009.
E. Shehab, A. Algergawy and A. Sarhan, “Accelerating relational database operations using both CPU and GPU co-processor,” Computers & Electrical Engineering, vol. 57, no. C, pp. 69–80, 2017.
H. H. O. Keh Kok Yong and V. V. Yap, “GPU SQL Query Accelerator,” International Journal of Information Technology, vol. 22, no. 1, pp. 1–18, 2016.
S. Breß, F. Beier, H. Rauhe, K.-U. Sattler, E. Schallehn and G. Saake, “Efficient co-processor utilization in database query processing,” Information Systems, vol. 38, no. 8, pp. 1084–1096, 2013.
Y. Chen, Z. Qiao, S. Davis, H. Jiang and K.-C. Li, “Pipelined Multi-GPU MapReduce for Big-Data Processing,” in Computer and Information Science. Studies in Computational Intelligence, vol 493, Heidelberg, 2013.
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Malhotra, S., Doja, M.N., Alam, B., Alam, M. (2018). Accelerating EGENMR Database Operations Using GPU Processing. In: Bhateja, V., Tavares, J., Rani, B., Prasad, V., Raju, K. (eds) Proceedings of the Second International Conference on Computational Intelligence and Informatics . Advances in Intelligent Systems and Computing, vol 712. Springer, Singapore. https://doi.org/10.1007/978-981-10-8228-3_62
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DOI: https://doi.org/10.1007/978-981-10-8228-3_62
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