A&S Graduate Studies

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Dissertation Proposal PDF: https://wmedu-my.sharepoint.com/personal/ygong07_wm_edu/_layouts/15/onedrive.aspx?login_hint=ygong07%40wm%2Eedu&id=%2Fpersonal%2Fygong07%5Fwm%5Fedu%2FDocuments%2FWM%5FDissertation%5Fproposal%5FYidong%2Epdf&parent=%2Fpersonal%2Fygong07%5Fwm%5Fedu%2FDocuments

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.

Bio:

I am a Ph.D. candidate in the Department of Computer Science at William & Mary, under the supervision of Prof. Pradeep Kumar. My research interests fall in deep learning systems, specifically high performance computing for sparse data and system-level optimization for GNN systems. Before coming to W&M, I got my master degree in computer science from Emory University.

Sponsored by: Computer Science