FGAQ: Accelerating Graph Analytical Queries Using FPGA

Yi Ding, Zhengyi Yang, Shunyang Li, Liuyi Chen, Haoran Ning, Kongzhang Hao, Yongfei Liu

Abstract: Field-programmable gate arrays (FPGAs) have significant advantages in parallelism and energy efficiency over CPUs and GPUs and are widely deployed by many enterprises and cloud server providers nowadays. In this paper, we demonstrate \(\textsf{FGAQ}\), an FPGA-based system for accelerating graph queries on massive graphs. \(\textsf{FGAQ}\) supports the two most fundamental types of graph queries, namely subgraph and path queries, and features 1) a CPU-FPGA co-designed framework, 2) a fully pipelined FPGA execution, and 3) reduced data transfer from FPGA’s external memory. \(\textsf{FGAQ}\) provides a user-friendly interface and significantly improved performance. Performance evaluation shows that \(\textsf{FGAQ}\) outperforms the most popular graph database, Neo4j, by up to three orders of magnitude. The demo video can be found at https://www.youtube.com/watch?v=pEkzw_DOQYE.