GPU-based efficient join algorithms on Hadoop

Author:

Wang HongzhiORCID,Li Ning,Wang Zheng,Li Jianing

Funder

NSFC

Publisher

Springer Science and Business Media LLC

Subject

Hardware and Architecture,Information Systems,Theoretical Computer Science,Software

Reference42 articles.

1. Afrati FN, Stasinopoulos N, Ullman JD, Vasilakopoulos A (2015) Sharesskew: an algorithm to handle skew for joins in mapreduce. CoRR. arXiv:abs/1512.03921

2. Angstadt K, Harcourt E (2015) A virtual machine model for accelerating relational database joins using a general purpose GPU. In: Watson LT, Weinbub J, Sosonkina M, Thacker WI (eds) Proceedings of the Symposium on High Performance Computing, HPC 2015, Part of the 2015 Spring Simulation Multiconference, SpringSim ’15, Alexandria, VA, USA, 12–15 April 2015. SCS/ACM, pp 127–134

3. Augustyn DR, Warchal L (2014) GPU-accelerated method of query selectivity estimation for non equi-join conditions based on discrete fourier transform. In: Bassiliades N, Ivanovic M, Kon-Popovska M, Manolopoulos Y, Palpanas T, Trajcevski G, Vakali A (eds) New Trends in Database and Information Systems II–Selected papers of the 18th East European Conference on Advances in Databases and Information Systems and Associated Satellite Events, ADBIS 2014 Ohrid, Macedonia, 7–10 Sept 2014 Proceedings II, volume 312 of Advances in Intelligent Systems and Computing. Springer, pp 215–227

4. Becerra S, Becerra SE, Schaefer AC, McInerney J, Cheng P (2014) Executing database queries using multiple processors. US Patent 8,762,366

5. Christos B, Anastasios G (2017) GPU processing of theta-joins. Concurr Comput Pract Exp 29(18):e4194

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