An empirical evaluation of exact set similarity join techniques using GPUs
作者：
Highlights：
• A thorough evaluation showing the sweet spot of each different technique for exact set similarity joins using a GPU.
• In large threshold values the sequential CPU techniques are competitive.
• In lower threshold values, employing parallel GPU techniques seems beneficial.
• Overall, GPU techniques may perform worse due to the imposed quadratic space overhead.
• A CPU-GPU co-process scheme performs better in some cases due to efficient workload balance.
摘要
•A thorough evaluation showing the sweet spot of each different technique for exact set similarity joins using a GPU.•In large threshold values the sequential CPU techniques are competitive.•In lower threshold values, employing parallel GPU techniques seems beneficial.•Overall, GPU techniques may perform worse due to the imposed quadratic space overhead.•A CPU-GPU co-process scheme performs better in some cases due to efficient workload balance.
论文关键词：Set-similarity join,GPU computing,CUDA
论文评审过程：Received 17 April 2019, Revised 6 December 2019, Accepted 10 December 2019, Available online 13 December 2019, Version of Record 23 December 2019.
论文官网地址：https://doi.org/10.1016/j.is.2019.101485
