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The problem becomes even more challenging when scaling to large graphs that exceed the capacity of single devices. Standard approaches to distributed DNN training, like data and model parallelism, do not directly apply to GNNs. Instead, two different approaches have emerged in the literature: whole-graph and sample-based training.<\/jats:p>\n          <jats:p>In this paper, we review and compare the two approaches. Scalability is challenging with both approaches, but we make a case that research should focus on sample-based training since it is a more promising approach. Finally, we review recent systems supporting sample-based training.<\/jats:p>","DOI":"10.1145\/3469379.3469387","type":"journal-article","created":{"date-parts":[[2021,6,6]],"date-time":"2021-06-06T12:43:56Z","timestamp":1622983436000},"page":"68-76","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":49,"title":["Scalable Graph Neural Network Training"],"prefix":"10.1145","volume":"55","author":[{"given":"Marco","family":"Serafini","sequence":"first","affiliation":[{"name":"University of Massachusetts Amherst, Amherst, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Guan","sequence":"additional","affiliation":[{"name":"University of Massachusetts Amherst, Amherst, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,6,6]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"265","volume-title":"12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16)","author":"Abadi Mart\u00edn","year":"2016","unstructured":"Mart\u00edn Abadi , Paul Barham , Jianmin Chen , Zhifeng Chen , Andy Davis , Jeffrey Dean , Matthieu Devin , Sanjay Ghemawat , Geoffrey Irving , Michael Isard , et al. 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