# Graph Processing on FPGAs: Taxonomy, Survey, Challenges

arXiv: https://arxiv.org/abs/1903.06697 · HTML: https://mlsanity.com/paper/1903.06697/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2019.

## Built on by (most cited first, 6 of 6)

- [Demystifying Chains, Trees, and Graphs of Thoughts](https://mlsanity.com/paper/2401.14295/) (2024)
- [Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis](https://mlsanity.com/paper/2205.09702/) (2022)
- [Charged particle tracking via edge-classifying interaction networks](https://mlsanity.com/paper/2103.16701/) (2021)
- [Graph Neural Networks for Charged Particle Tracking on FPGAs](https://mlsanity.com/paper/2112.02048/) (2021)
- [GraphMineSuite: Enabling High-Performance and Programmable Graph Mining Algorithms with Set Algebra](https://mlsanity.com/paper/2103.03653/) (2021)
- [Demystifying Higher-Order Graph Neural Networks](https://mlsanity.com/paper/2406.12841/) (2024)

## Abstract (arXiv)

Graph processing has become an important part of various areas, such as machine learning, computational sciences, medical applications, social network analysis, and many others. Various graphs, for example web or social networks, may contain up to trillions of edges. The sheer size of such datasets, combined with the irregular nature of graph processing, poses unique challenges for the runtime and the consumed power. Field Programmable Gate Arrays (FPGAs) can be an energy-efficient solution to deliver specialized hardware for graph processing. This is reflected by the recent interest in developing various graph algorithms and graph processing frameworks on FPGAs. To facilitate understanding of this emerging domain, we present the first survey and taxonomy on graph computations on FPGAs. Our survey describes and categorizes existing schemes and explains key ideas. Finally, we discuss research and engineering challenges to outline the future of graph computations on FPGAs.