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Consequently, existing frameworks capable of efficiently training large GNN models usually incur a significant accuracy degradation because of the currently-available shortcuts involved. To address these limitations, we instead propose FreshGNN, a general-purpose GNN mini-batch training framework that leverages a historical cache for storing and reusing GNN node embeddings instead of re-computing them through fetching raw features at every iteration. Critical to its success, the corresponding cache policy is designed, using a combination of gradient-based and staleness criteria, to selectively screen those embeddings which are relatively stable and can be cached, from those that need to be re-computed to reduce estimation errors and subsequent downstream accuracy loss. When paired with complementary system enhancements to support this selective historical cache, FreshGNN is able to accelerate the training speed on large graph datasets such as ogbn-papers100M and MAG240M by 3.4\u00d7 up to 20.5\u00d7 and reduce the memory access by 59%, with less than 1% influence on test accuracy.<\/jats:p>","DOI":"10.14778\/3648160.3648184","type":"journal-article","created":{"date-parts":[[2024,5,3]],"date-time":"2024-05-03T21:52:53Z","timestamp":1714773173000},"page":"1473-1486","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["FreshGNN: Reducing Memory Access via Stable Historical Embeddings for Graph Neural Network Training"],"prefix":"10.14778","volume":"17","author":[{"given":"Kezhao","family":"Huang","sequence":"first","affiliation":[{"name":"Tsinghua University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haitian","family":"Jiang","sequence":"additional","affiliation":[{"name":"New York University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minjie","family":"Wang","sequence":"additional","affiliation":[{"name":"Amazon"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangxuan","family":"Xiao","sequence":"additional","affiliation":[{"name":"MIT"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Wipf","sequence":"additional","affiliation":[{"name":"Amazon"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Song","sequence":"additional","affiliation":[{"name":"Amazon"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quan","family":"Gan","sequence":"additional","affiliation":[{"name":"Amazon"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zengfeng","family":"Huang","sequence":"additional","affiliation":[{"name":"Fudan University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jidong","family":"Zhai","sequence":"additional","affiliation":[{"name":"Tsinghua University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Amazon"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,5,3]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447786.3456233"},{"key":"e_1_2_1_2_1","first-page":"392","volume-title":"Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming","author":"Cai Zhenkun","year":"2023","unstructured":"Zhenkun Cai, Qihui Zhou, Xiao Yan, Da Zheng, Xiang Song, Chenguang Zheng, James Cheng, and George Karypis. 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