← Back to all news
arXiv Machine Learning By Deifilia Kieckhefen, Juan Pedro Guti\'errez Hermosillo Muriedas, Lars Helge Heyen, Mathis Bode, Iida Hakulinen, Andreas Herten, Chelsea Maria John, Thorsten Kurth, Anni Moisala, Asena Karolin \"Ozdemir, Kaleb Phipps, Oskar Taubert, Arvid Weyrauch, Markus G\"otz, Charlotte Debus

4D Parallelism Unlocks Exascale Bayesian Neural Networks for High-Fidelity Atmospheric Modeling

Read the original on arXiv Machine Learning →

BEAST is the first Bayesian Swin Transformer for atmospheric forecasting at 0.25° global resolution, capable of quantifying both aleatoric and epistemic uncertainty. The authors introduce a 4D‑parallelization scheme with domain‑tensor‑parallelism and a novel uncertainty parallel method, allowing a 2.4‑billion‑parameter model to reach 3.96 EFLOP/s on 20,480 NVIDIA GH200 GPUs. A 700‑million‑parameter BEAST trained on 40 years of data achieves predictive skill comparable to state‑of‑the‑art probabilistic AI models, predicts extreme events with high accuracy, and generates large ensembles 3–4× faster than the current best AI model.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv AI
Jun 11

AI4Land: Scalable Deep Learning for Global High-Resolution Land Use Reconstruction

arXiv:2606. 11793v1 Announce Type: cross Abstract: Uncertainty in the terrestrial carbon cycle remains a major constraint in climate projections, partly driven by the uncertainties affecting the land surface representation and variability in Earth system models.

By Amirpasha Mozaffari, Marina Casta\~no, Stefano Materia, Etienne Tourigny, Oscar Molina-Sedano, Jordi Varela-Agrelo, Dario Garcia-Gasulla, Miguel Castrillo Melguizo, Mario Acosta, Amanda Duarte
More like this →
arXiv AI
Sep 2

Global Attention with Linear Complexity for Exascale Generative Data Assimilation in Earth System Prediction

The paper introduces STORM, a one‑stage generative AI framework that reformulates Earth system data assimilation as diffusion‑based Bayesian posterior sampling, replacing costly PDE ensemble forecasts with scalable AI inference. STORM employs a spatiotemporal transformer with a global‑attention algorithm that reduces computational complexity from quadratic to linear, enabling high‑resolution, long‑context modeling. The system scales to 74,400 GPUs on Frontier, achieving 96–99 % strong‑scaling efficiency and up to 6 ExaFLOPs sustained BF16 throughput, while supporting 32,768‑member ensembles for uncertainty quantification in just 34 seconds on 4,096 GPUs, and demonstrates improved hurricane tracking and climate reanalysis accuracy.

By Xiao Wang, Zezhong Zhang, Isaac Lyngaas, Hong-Jun Yoon, Jong-Youl Choi, Siming Liang, Janet Wang, Hristo G. Chipilski, Ashwin M. Aji, Feng Bao, Peter Jan van Leeuwen, Dan Lu, Guannan Zhang
llmsdiffusionefficiency
More like this →