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DAHA: Accelerating GNN Training with Data and Hardware Aware Execution Planning

Summary: DAHA: lightweight data+hardware-aware cost model predicting per-op runtimes to guide execution planning for mini-batch GNN training. Combines group in-turn pipelining, intra-batch rewriting, and inter-batch scheduling to eliminate batch-prep/transfer bottlenecks and boost device utilization and pipeline parallelism. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13381
Venue
VLDB
Year
2024
Pagerank
4.7747168e-05
Overall Rank
7,289 | 49.30%
DOI
10.14778/3648160.3648176

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