# Exascale Deep Learning for Climate Analytics

arXiv: https://arxiv.org/abs/1810.01993 · HTML: https://mlsanity.com/paper/1810.01993/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2018.

## Built on by (most cited first, 28 of 28)

- [Graph Neural Controlled Differential Equations for Traffic Forecasting](https://mlsanity.com/paper/2112.03558/) (2021)
- [Will Artificial Intelligence supersede Earth System and Climate Models?](https://mlsanity.com/paper/2101.09126/) (2021)
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- [Explainable GeoAI: Can saliency maps help interpret artificial intelligence's learning process? An empirical study on natural feature detection](https://mlsanity.com/paper/2303.09660/) (2023)
- [Graph Neural Rough Differential Equations for Traffic Forecasting](https://mlsanity.com/paper/2303.10909/) (2023)
- [Learning from Predictions: Fusing Training and Autoregressive Inference for Long-Term Spatiotemporal Forecasts](https://mlsanity.com/paper/2302.11101/) (2023)
- [JUWELS Booster -- A Supercomputer for Large-Scale AI Research](https://mlsanity.com/paper/2108.11976/) (2021)
- [PCE-PINNs: Physics-Informed Neural Networks for Uncertainty Propagation in Ocean Modeling](https://mlsanity.com/paper/2105.02939/) (2021)
- [COMET: A Novel Memory-Efficient Deep Learning Training Framework by Using Error-Bounded Lossy Compression](https://mlsanity.com/paper/2111.09562/) (2021)
- [Adaptive learning of effective dynamics: Adaptive real-time, online modeling for complex systems](https://mlsanity.com/paper/2304.01732/) (2023)
- [EvoMesh: Adaptive Physical Simulation with Hierarchical Graph Evolutions](https://mlsanity.com/paper/2410.03779/) (2024)
- [Analysis, Characterization, Prediction and Attribution of Extreme Atmospheric Events with Machine Learning: a Review](https://mlsanity.com/paper/2207.07580/) (2022)
- [Deep NURBS -- Admissible Physics-informed Neural Networks](https://mlsanity.com/paper/2210.13900/) (2022)
- [AI Benchmark Democratization and Carpentry](https://mlsanity.com/paper/2512.11588/) (2025)
- [An Oracle for Guiding Large-Scale Model/Hybrid Parallel Training of Convolutional Neural Networks](https://mlsanity.com/paper/2104.09075/) (2021)
- [Scalable Artificial Intelligence for Science: Perspectives, Methods and Exemplars](https://mlsanity.com/paper/2406.17812/) (2024)
- [KAKURENBO: Adaptively Hiding Samples in Deep Neural Network Training](https://mlsanity.com/paper/2310.10102/) (2023)
- [Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: A Multiobjective Hyperparameter and Architecture Optimization Approach](https://mlsanity.com/paper/2404.05768/) (2024)
- [SOLAR: A Highly Optimized Data Loading Framework for Distributed Training of CNN-based Scientific Surrogates](https://mlsanity.com/paper/2211.00224/) (2022)
- [SAIH: A Scalable Evaluation Methodology for Understanding AI Performance Trend on HPC Systems](https://mlsanity.com/paper/2212.03410/) (2022)
- [Unsupervised Discovery of Extreme Weather Events Using Universal Representations of Emergent Organization](https://mlsanity.com/paper/2304.12586/) (2023)
- [Evaluating Loss Functions and Learning Data Pre-Processing for Climate Downscaling Deep Learning Models](https://mlsanity.com/paper/2306.11144/) (2023)
- [Analyzing the Impact of Climate Change With Major Emphasis on Pollution: A Comparative Study of ML and Statistical Models in Time Series Data](https://mlsanity.com/paper/2405.15835/) (2024)
- [GAIA: A Foundation Model for Operational Atmospheric Dynamics](https://mlsanity.com/paper/2505.18179/) (2025)

## Abstract (arXiv)

We extract pixel-level masks of extreme weather patterns using variants of Tiramisu and DeepLabv3+ neural networks. We describe improvements to the software frameworks, input pipeline, and the network training algorithms necessary to efficiently scale deep learning on the Piz Daint and Summit systems. The Tiramisu network scales to 5300 P100 GPUs with a sustained throughput of 21.0 PF/s and parallel efficiency of 79.0%. DeepLabv3+ scales up to 27360 V100 GPUs with a sustained throughput of 325.8 PF/s and a parallel efficiency of 90.7% in single precision. By taking advantage of the FP16 Tensor Cores, a half-precision version of the DeepLabv3+ network achieves a peak and sustained throughput of 1.13 EF/s and 999.0 PF/s respectively.