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02.11.2021 | Regular Paper

Accelerating multi-way joins on the GPU

verfasst von: Zhuohang Lai, Xibo Sun, Qiong Luo, Xiaolong Xie

Erschienen in: The VLDB Journal | Ausgabe 3/2022

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Abstract

Graphic processing units (GPUs) have been employed as hardware accelerators for online analytics. However, multi-way joins, which are common in analytic workloads, are inefficient on GPUs. Therefore, we propose to accelerate two representative multi-way join algorithms on the GPU: a multi-way hash join (MHJ) and the worst-case optimal Leapfrog Triejoin (LFTJ). Specifically, we design a warp-based parallelization strategy to reduce thread divergence and to facilitate coalesced memory access in parallel searches in a table. We further enhance our implementations with a set of GPU-friendly optimizations, including dynamic workload sharing among threads and elimination of the result counting phase. Additionally, we enable out-of-core multi-way joins with software pipelining. Our experiments show that our optimized MHJ and LFTJ outperform the state-of-the-art GPU algorithms by a factor of up to 67 on an NVIDIA V100 GPU.

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Titel: Accelerating multi-way joins on the GPU
verfasst von: Zhuohang Lai
Xibo Sun
Qiong Luo
Xiaolong Xie
Publikationsdatum: 02.11.2021
Verlag: Springer Berlin Heidelberg
Erschienen in: The VLDB Journal / Ausgabe 3/2022
Print ISSN: 1066-8888
Elektronische ISSN: 0949-877X
DOI: https://doi.org/10.1007/s00778-021-00708-y

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