SWASH: A Flexible Communication Framework with Sliding Window-Based Cache Sharing for Scalable DGNN Training
Summary: SWASH: sliding-window cache sharing for scalable distributed DGNN training. Lightweight, sliding-window-aware partitioning with adaptive scheduling and boundary-embedding caches reduces partitioning/communication overhead while preserving accuracy, yielding 9.44x speedups over state-of-the-art.
(summarized by gpt-5-nano on Feb 09 2026)
@inproceedings{song_sigmod25,
title = {{SWASH: A Flexible Communication Framework with Sliding Window-Based Cache Sharing for Scalable DGNN Training}},
author = {Song, Zhen and Gu, Yu and Li, Tianyi and Li, Yushuai and Sun, Qing and Zhang, Yanfeng and Jensen, Christian S. and Yu, Ge},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3725360},
url = {https://dl.acm.org/doi/10.1145/3725360},
year = {2025}
}
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