Scalable and Adaptive Parallel Training of Graph Transformer on Large Graphs
Graph foundation models have demonstrated remarkable adaptability across diverse downstream tasks through large-scale pretraining on graphs. However, existing implementations of the backbone model, graph transformers, are typically limited to single-GPU systems, leading to long training times or out-of-memory issues on large graphs. Moreover, parallelizing graph transformer training over the full graph is challenging, as efficiency depends heavily on both the graph structure and system characteristics, such as bandwidth and memory capacity. In this work, we introduce a distributed training framework for graph transformers, which automatically selects and optimizes parallelization strategi...
| Authors | Jun-Liang Lin, Kamesh Madduri, Mahmut Taylan Kandemir |
|---|---|
| Primary category | cs.DC |
| Published | 17 April 2026 |
| Last updated at source | 17 April 2026 |
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