Single-GPU GNN Systems: Traps and Pitfalls
<|reference_start|>Single-GPU GNN Systems: Traps and Pitfalls: The current graph neural network (GNN) systems have established a clear trend of not showing training accuracy results, and directly or indirectly relying on smaller datasets for evaluations majorly. Our in-depth analysis shows that it leads to a chain of pitfalls in the system design and evaluation process, questioning the practicality of many of the proposed system optimizations, and affecting conclusions and lessons learned. We analyze many single-GPU systems and show the fundamental impact of these pitfalls. We further develop hypotheses, recommendations, and evaluation methodologies, and provide future directions. Finally, a new reference system is developed to establish a new line of optimizations rooted in solving the system-design pitfalls efficiently and practically. The proposed design can productively be integrated into prior works, thereby truly advancing the state-of-the-art.<|reference_end|>
@article{gong2024single-gpu,
    title={Single-GPU GNN Systems: Traps and Pitfalls},
    author={Yidong Gong, Arnab Tarafder, Saima Afrin, and Pradeep Kumar},
    journal={arXiv preprint arXiv:2402.03548},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.03548},
    primaryClass={cs.LG cs.DC}
}