Accelerating multi-way joins on the GPU-Reference-Cited by-同舟云学术

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Accelerating multi-way joins on the GPU

Published:2021-11-02 Issue: Volume: Page:
ISSN:1066-8888
Container-title:The VLDB Journal
language:en
Short-container-title:The VLDB Journal

Author:

Lai Zhuohang^ORCID,Sun Xibo,Luo Qiong,Xie Xiaolong

Funder

Alibaba Group

Hong Kong Research Grants Council

Publisher

Springer Science and Business Media LLC

Subject

Hardware and Architecture,Information Systems

Link

https://link.springer.com/content/pdf/10.1007/s00778-021-00708-y.pdf

Reference60 articles.

1. Aberger, C.R., Lamb, A., Tu, S., Nötzli, A., Olukotun, K., Ré, C.: Emptyheaded: a relational engine for graph processing. ACM Trans. Database Syst. 42(4), 20:1-20:44 (2017). https://doi.org/10.1145/3129246

2. Aghajarian, D., Puri, S., Prasad, S.K.: GCMF: an efficient end-to-end spatial join system over large polygonal datasets on GPGPU platform. In: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 18:1–18:10 (2016). https://doi.org/10.1145/2996913.2996982

3. Alcantara, D.A., Sharf, A., Abbasinejad, F., Sengupta, S., Mitzenmacher, M., Owens, J.D., Amenta, N.: Real-time parallel hashing on the GPU. ACM Trans. Graph. 28(5), 154 (2009). https://doi.org/10.1145/1618452.1618500

4. Alcantara, D.A., Volkov, V., Sengupta, S., Mitzenmacher, M., Owens, J.D., Amenta, N.: Building an efficient hash table on the GPU. In: GPU Computing Gems Jade Edition, pp. 39–53 (2012)

5. Appleby, A.: Murmurhash. http://code.google.com/p/smhasher/

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