Experiments on QM9 and AFLOW datasets show that static and dynamic batching for GNNs can yield up to 2.7x training speedups depending on data, model, batch size, hardware, and training steps, with occasional differences in learning metrics.
Accurate modeling of the potential energy surface of atmospheric molecular clusters boosted by neural networks.Environmental Science: Advances 3, 10 (2024), 1438–1451
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Training speedups via batching for geometric learning: an analysis of static and dynamic algorithms
Experiments on QM9 and AFLOW datasets show that static and dynamic batching for GNNs can yield up to 2.7x training speedups depending on data, model, batch size, hardware, and training steps, with occasional differences in learning metrics.