4D Parallelism Unlocks Exascale Bayesian Neural Networks for High-Fidelity Atmospheric Modeling
An orthogonal 4D-parallelization scheme is devised that introduces a unique domain-tensor-parallelism strategy and a novel uncertainty parallel method, enabling the potential of high-fidelity uncertainty quantification in atmospheric AI models, heralding a new era for AI-based models in climate and Earth system science...
Deifilia Kieckhefen, J. P. G. H. Muriedas, L. Heyen et al.
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