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Free Your Hash Join From Slow Synchronizations: A Fast Hash Join Implementation on GPU

Published: 30 July 2024 Publication History

Abstract

Non-partitioned hash join on GPU suffers from significant overhead from two types of synchronizations, i.e., (i) synchronizing concurrent writes in build phase and (ii) memory stall of synchronous random memory access in probe phase. To address these problems, we introduce a synchronization-free hash join implementation. The hash table is constructed in a lock-free way in build phase and searched with asynchronous memory access in probe phase. Through these innovations, we demonstrate a substantial improvement in hash join performance on GPUs, achieving up to a 1.19x speedup compared to traditional approaches.

References

[1]
Yangshen Deng, Shiwen Chen, Zhaoyang Hong, and Bo Tang. 2024. How Does Software Prefetching Work on GPU Query Processing?. In DaMoN. 9 pages.
[2]
Henning Funke and Jens Teubner. 2020. Data-parallel query processing on non-uniform data. Proc. VLDB Endow. 13, 6 (2020), 884–897.
[3]
Panagiotis Sioulas, Periklis Chrysogelos, Manos Karpathiotakis, Raja Appuswamy, and Anastasia Ailamaki. 2019. Hardware-Conscious Hash-Joins on GPUs. In ICDE. 698–709.

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ACM-TURC '24: Proceedings of the ACM Turing Award Celebration Conference - China 2024
July 2024
261 pages
ISBN:9798400710117
DOI:10.1145/3674399
This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 July 2024

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