mlsanityData to 7 Oct 2026

Exascale Deep Learning for Climate Analytics

arXiv 1810.01993 ↗First posted 3 Oct 2018cs.DCcited by 28

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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.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Oct 2018.

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Citing papers per year: 2021: 11; 2022: 4; 2023: 7; 2024: 4; 2025: 2.

28 papers.

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  1. 2021Graph Neural Controlled Differential Equations for Traffic Forecastingcited by 133
  2. 2021Will Artificial Intelligence supersede Earth System and Climate Models?cited by 37
  3. 2021An Efficient Statistical-based Gradient Compression Technique for Distributed Training Systemscited by 32
  4. 2021Climate Modeling with Neural Diffusion Equationscited by 20
  5. 2021Bridging observation, theory and numerical simulation of the ocean using Machine Learningcited by 15
  6. 2021MLPerf HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systemscited by 15
  7. 2021Clairvoyant Prefetching for Distributed Machine Learning I/Ocited by 14
  8. 2023Explainable GeoAI: Can saliency maps help interpret artificial intelligence's learning process? An empirical study on natural feature detectioncited by 14
  9. 2023Graph Neural Rough Differential Equations for Traffic Forecastingcited by 14
  10. 2023Learning from Predictions: Fusing Training and Autoregressive Inference for Long-Term Spatiotemporal Forecastscited by 11
  11. 2021JUWELS Booster -- A Supercomputer for Large-Scale AI Researchcited by 10
  12. 2021PCE-PINNs: Physics-Informed Neural Networks for Uncertainty Propagation in Ocean Modelingcited by 8
16 more
  1. 2021COMET: A Novel Memory-Efficient Deep Learning Training Framework by Using Error-Bounded Lossy Compressioncited by 8
  2. 2023Adaptive learning of effective dynamics: Adaptive real-time, online modeling for complex systemscited by 4
  3. 2024EvoMesh: Adaptive Physical Simulation with Hierarchical Graph Evolutionscited by 4
  4. 2022Analysis, Characterization, Prediction and Attribution of Extreme Atmospheric Events with Machine Learning: a Reviewcited by 3
  5. 2022Deep NURBS -- Admissible Physics-informed Neural Networkscited by 3
  6. 2025AI Benchmark Democratization and Carpentrycited by 3
  7. 2021An Oracle for Guiding Large-Scale Model/Hybrid Parallel Training of Convolutional Neural Networkscited by 2
  8. 2024Scalable Artificial Intelligence for Science: Perspectives, Methods and Exemplarscited by 2
  9. 2023KAKURENBO: Adaptively Hiding Samples in Deep Neural Network Trainingcited by 1
  10. 2024Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: A Multiobjective Hyperparameter and Architecture Optimization Approachcited by 1
  11. 2022SOLAR: A Highly Optimized Data Loading Framework for Distributed Training of CNN-based Scientific Surrogatescited by 0
  12. 2022SAIH: A Scalable Evaluation Methodology for Understanding AI Performance Trend on HPC Systemscited by 0
  13. 2023Unsupervised Discovery of Extreme Weather Events Using Universal Representations of Emergent Organizationcited by 0
  14. 2023Evaluating Loss Functions and Learning Data Pre-Processing for Climate Downscaling Deep Learning Modelscited by 0
  15. 2024Analyzing the Impact of Climate Change With Major Emphasis on Pollution: A Comparative Study of ML and Statistical Models in Time Series Datacited by 0
  16. 2025GAIA: A Foundation Model for Operational Atmospheric Dynamicscited by 0