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Accelerating multi-way joins on the GPU by Zhuohang Lai & Xibo Sun & Qiong Luo & Xiaolong Xie ISBN 101007/S0077802100708Y instant download

  • SKU: EBN-232487178
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Instant download (eBook) Accelerating multi-way joins on the GPU after payment.
Authors:Zhuohang Lai & Xibo Sun & Qiong Luo & Xiaolong Xie
Pages:updating ...
Year:2022
Publisher:Springer Berlin Heidelberg
Language:english
File Size:5.31 MB
Format:pdf
ISBNS:101007/S0077802100708Y
Categories: Ebooks

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Accelerating multi-way joins on the GPU by Zhuohang Lai & Xibo Sun & Qiong Luo & Xiaolong Xie ISBN 101007/S0077802100708Y instant download

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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