FGAQ: Accelerating Graph Analytical Queries Using FPGA
Yi Ding, Zhengyi Yang*, Shunyang Li, Liuyi Chen, Haoran Ning, Kongzhang Hao, Yongfei Liu
RAIDS Lab Authors
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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 FGAQ, an FPGA-based system for accelerating graph queries on massive graphs. 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. FGAQ provides a user-friendly interface and significantly improved performance. Performance evaluation shows that 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.
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BibTeX
@inproceedings{ding2024fgaq,
title = {FGAQ: Accelerating Graph Analytical Queries Using FPGA},
author = {Ding, Yi and Yang, Zhengyi and Li, Shunyang and Chen, Liuyi and Ning, Haoran and Hao, Kongzhang and Liu, Yongfei},
editor = {Zhang, Wenjie and Tung, Anthony and Zheng, Zhonglong and Yang, Zhengyi and Wang, Xiaoyang and Guo, Hongjie},
booktitle = {Web and Big Data},
year = {2024},
publisher = {Springer Nature Singapore},
address = {Singapore},
pages = {357--361},
isbn = {978-981-97-7244-5}
}