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However, due to the irregular nature of the graph structure, existing distributed approaches suffer from unbalanced workloads and high overhead in managing cross-worker vertex dependencies.<\/jats:p>\n          <jats:p>In this paper, we leverage tensor parallelism for distributed GNN training. GNN tensor parallelism eliminates cross-worker vertex dependencies by partitioning features instead of graph structures. Different workers are assigned training tasks on different feature slices with the same dimensional size, leading to a complete load balance. We achieve efficient GNN tensor parallelism through two critical functions. Firstly, we employ a generalized decoupled training framework to decouple NN operations from graph aggregation operations, significantly reducing the communication overhead caused by NN operations which must be computed using complete features. Secondly, we employ a memory-efficient task scheduling strategy to support the training of large graphs exceeding single GPU memory, while further improving performance by overlapping communication and computation. By integrating the above techniques, we propose a distributed GNN training system NeutronTP. Our experimental results on a 16-node Aliyun cluster demonstrate that NeutronTP achieves 1.29\u00d7-8.72\u00d7 speedup over state-of-the-art GNN systems including DistDGL, NeutronStar, and Sancus.<\/jats:p>","DOI":"10.14778\/3705829.3705837","type":"journal-article","created":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T23:21:06Z","timestamp":1740784866000},"page":"173-186","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["NeutronTP: Load-Balanced Distributed Full-Graph GNN Training with Tensor Parallelism"],"prefix":"10.14778","volume":"18","author":[{"given":"Xin","family":"Ai","sequence":"first","affiliation":[{"name":"Northeastern University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Yuan","sequence":"additional","affiliation":[{"name":"Northeastern University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zeyu","family":"Ling","sequence":"additional","affiliation":[{"name":"Northeastern University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiange","family":"Wang","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanfeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Northeastern University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenbo","family":"Fu","sequence":"additional","affiliation":[{"name":"Northeastern University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaoyi","family":"Chen","sequence":"additional","affiliation":[{"name":"Northeastern University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Gu","sequence":"additional","affiliation":[{"name":"Northeastern University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ge","family":"Yu","sequence":"additional","affiliation":[{"name":"Northeastern University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,2,28]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009951412412"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3303431"},{"key":"e_1_2_1_3_1","volume-title":"Maximizing Parallelism in Distributed Training for Huge Neural Networks. 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