Computer Science > Hardware Architecture
arXiv:2408.01902 (cs)
[Submitted on 4 Aug 2024 (v1), last revised 18 Jan 2025 (this version, v3)]
Title:Survey on Characterizing and Understanding GNNs from a Computer Architecture Perspective
View a PDF of the paper titled Survey on Characterizing and Understanding GNNs from a Computer Architecture Perspective, by Meng Wu and 5 other authors
View PDFHTML (experimental)Abstract:Characterizing and understanding graph neural networks (GNNs) is essential for identifying performance bottlenecks and facilitating their deployment in parallel and distributed systems. Despite substantial work in this area, a comprehensive survey on characterizing and understanding GNNs from a computer architecture perspective is lacking. This work presents a comprehensive survey, proposing a triple-level classification method to categorize, summarize, and compare existing efforts, particularly focusing on their implications for parallel architectures and distributed systems. We identify promising future directions for GNN characterization that align with the challenges of optimizing hardware and software in parallel and distributed systems. Our survey aims to help scholars systematically understand GNN performance bottlenecks and execution patterns from a computer architecture perspective, thereby contributing to the development of more efficient GNN implementations across diverse parallel architectures and distributed systems.
| Comments: | To appear in IEEE Transactions on Parallel and Distributed Systems |
| Subjects: | Hardware Architecture (cs.AR) |
| Cite as: | arXiv:2408.01902 [cs.AR] |
| (orarXiv:2408.01902v3 [cs.AR] for this version) | |
| https://doi.org/10.48550/arXiv.2408.01902 arXiv-issued DOI via DataCite |
Submission history
From: Mingyu Yan [view email][v1] Sun, 4 Aug 2024 02:12:51 UTC (3,634 KB)
[v2] Fri, 16 Aug 2024 01:36:22 UTC (3,522 KB)
[v3] Sat, 18 Jan 2025 03:06:39 UTC (3,917 KB)
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View a PDF of the paper titled Survey on Characterizing and Understanding GNNs from a Computer Architecture Perspective, by Meng Wu and 5 other authors
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