5 papers
4D Parallelism Unlocks Exascale Bayesian Neural Networks for High-Fidelity Atmospheric Modeling
Deifilia Kieckhefen, Juan Pedro Gutiérrez Hermosillo Muriedas, Lars Helge Heyen +12
We present BEAST, the first-ever Bayesian Swin Transformer for atmospheric forecasting on 0.25 global resolution able to accurately quantify both aleatoric and epistemic un…
Fourier Neural Operators for Rayleigh-Bénard Convection
Chelsea Maria John, Thibaut Lunet, Sebastian Götschel +3
We propose an improved Fourier Neural Operator (FNO) for modeling two-dimensional Rayleigh-Bénard convection by predicting time increments instead of full solutions, achieving high…
Training LLMs on HPC Systems: Best Practices from the OpenGPT-X Project
Carolin Penke, Chelsea Maria John, Jan Ebert +2
The training of large language models (LLMs) requires substantial computational resources, complex software stacks, and carefully designed workflows to achieve scalability and effi…
Performance and Power: Systematic Evaluation of AI Workloads on Accelerators with CARAML
Chelsea Maria John, Stepan Nassyr, Carolin Penke +1
The rapid advancement of machine learning (ML) technologies has driven the development of specialized hardware accelerators designed to facilitate more efficient model training. Th…
Application-Driven Exascale: The JUPITER Benchmark Suite
Andreas Herten, Sebastian Achilles, Damian Alvarez +28
Benchmarks are essential in the design of modern HPC installations, as they define key aspects of system components. Beyond synthetic workloads, it is crucial to include real appli…