Efficiently finding subgraph embeddings in large graphs is crucial for many application areas like biology and social network analysis. Set intersections are the predominant and most challenging aspect of current join-based subgraph query processing systems for CPUs. Previous work has shown the viability of utilizing FPGAs for acceleration of graph and join processing. In this work, we propose GraphMatch, the first genearl-purpose stand-alone subgraph query processing accelerator based on worst-case optimal joins (WCOJ) that is fully designed for modern, field programmable gate array (FPGA) hardware. For efficient processing of various graph data sets and query graph patterns, it leverages a novel set intersection approach, called AllCompare, tailor-made for FPGAs. We show that this set intersection approach efficiently solves multi-set intersections in subgraph query processing, superior to CPU-based approaches. Overall, GraphMatch achieves a speedup of over 2.68x and 5.16x, compared to the state-of-the-art systems GraphFlow and RapidMatch, respectively.

翻译：在大图中高效查找子图嵌入对于生物信息学、社交网络分析等诸多应用领域至关重要。集合交集是当前基于CPU的联接式子图查询处理系统中最主要且最具挑战性的环节。此前研究已证明了利用FPGA加速图和联接处理的可行性。本文提出GraphMatch——首个基于最坏情况最优联接（WCOJ）的通用独立子图查询处理加速器，专为现代现场可编程门阵列（FPGA）硬件设计。为高效处理不同图数据集和查询图模式，它采用了一种名为AllCompare的、专为FPGA定制的新型集合交集方法。我们证明，该集合交集方法能有效解决子图查询处理中的多集合交集问题，且性能优于基于CPU的方法。总体而言，与当前最先进的系统GraphFlow和RapidMatch相比，GraphMatch分别实现了超过2.68倍和5.16倍的加速比。