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arXiv · 2402.03548

Single-GPU GNN Systems: Traps and Pitfalls

Yidong Gong·Arnab Tarafder·Saima Afrin·Pradeep Kumar
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Abstract

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.

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Yidong Gong, Arnab Tarafder, Saima Afrin, Pradeep Kumar. 2024-02-05. Single-GPU GNN Systems: Traps and Pitfalls. https://arxiv.org/abs/2402.03548

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