  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.
