Exact set similarity join is a notoriously expensive operation, for which several solutions have been proposed. Recently, there have been studies that present a comparative analysis using MapReduce or a non-parallel setting. Our contribution is that we complement these works through conducting a thorough evaluation of the state-of-the-art GPU-enabled techniques. These techniques are highly diverse in their key features and our experiments manage to reveal the key strengths of each one. As we explain, in real-life applications there is no dominant solution. Depending on specific dataset and query characteristics, each solution, even not using the GPU at all, has its own sweet spot. All our work is repeatable and extensible.

确切的集合相似性联接是一个众所周知的昂贵操作，为此已经提出了几种解决方案。最近，有研究提出使用MapReduce或非平行设置进行比较分析。我们的贡献是，我们通过对最新的GPU支持技术进行全面评估来补充这些工作。这些技术的关键特征千差万别，我们的实验设法揭示了每种技术的关键优势。正如我们所解释的，在现实生活中，没有主流解决方案。根据特定的数据集和查询特征，每个解决方案（甚至根本不使用GPU）都有其自身的优势。我们所有的工作都是可重复和可扩展的。