Papers for

supercomputer operators

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Interactive 3d digital twin helps visualize supercomputer hardware and activity

Object Model Analysis of a Supercomputer with Digital Twin

Abstract: Operators and developers need a mental model of both the structure and the live behavior of a large supercomputer, but its physical layout, logical organization, and streams of per-node telemetry are difficult to relate to one another, making it hard to trace a metric or event back to a specific hardware component. We present DAT, an interactive three-dimensional digital analytics twin of a compute cluster built in a real-time game engine, Unreal Engine. DAT expands a compact, parametric description of a supercomputer, a reusable Digital Twin Prototype (DTP), into a navigable Digital Twin Instance (DTI) that mirrors its physical containment hierarchy of racks, chassis, blades, and network links, encoding each node's role and health in its appearance, while a lightweight event-driven simulator animates job and hardware activity over a virtual clock. Our current implementation adds a two-path node-selection mechanism, unifying direct 3D pointing with command-shell queries, that opens an in-world visual-analytics panel beside any selected component showing summary statistics and live, time-varying metrics. We describe this architecture, report qualitative behavior from the working prototype, and outline the path toward driving the panels with recorded telemetry and in-situ anomaly detection.

Fri 11 SeptDistributed, Parallel, and Cluster Computing
The gist
Supercomputers are complex machines made of many parts, and it can be hard to understand how all these parts work together or spot problems. The authors created a 3D digital twin, a virtual model, that shows the supercomputer’s physical setup like racks and network links, along with live activity and health information. This model runs in a game engine and lets users explore and select parts either by clicking in 3D or using commands, showing detailed stats and ongoing metrics. This helps operators and developers quickly connect performance or problems to the right hardware piece.
Open → 2609.13571v1

Bayesian neural network achieves faster precise weather forecasts with uncertainty

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

Abstract: We present BEAST, the first-ever Bayesian Swin Transformer for atmospheric forecasting on 0.25$^\circ$ global resolution able to accurately quantify both aleatoric and epistemic uncertainty. To overcome the associated computational bottlenecks, we devise an orthogonal 4D-parallelization scheme that introduces a unique domain-tensor-parallelism strategy and a novel uncertainty parallel method, enabling us to fully leverage GPU capacity and efficiently scale model training. For a 2.4-billion-parameter model, we achieve a peak performance of 3.96 EFLOP/s on 20,480 NVIDIA GH200 GPUs on the JUPITER supercomputer. We train BEAST as a 700-million-parameter model with 96 random weight samples on 384 nodes on 40 years of data for nearly one million gradient updates. This model achieves predictive skill scores competitive with state-of-the-art probabilistic atmospheric AI models and numerical models, and can predict extreme events with exceptional skill, while generating large ensembles 3 to 4 times faster than the current-best AI model. Our contribution unlocks the potential of high-fidelity uncertainty quantification in atmospheric AI models, heralding a new era for AI-based models in climate and Earth system sciences.

Fri 11 SeptArtificial IntelligenceDistributed, Parallel, and Cluster ComputingMachine Learning
The gist
Weather forecasting models need to predict not just what will happen, but also how sure they are about their predictions. The authors created BEAST, a new kind of AI model that uses a special neural network to make highly detailed weather forecasts across the globe and shows how confident it is in those forecasts. They also developed a way to run this huge model efficiently on many GPUs at once, making training faster and more powerful. Their results show the model can predict extreme weather well and generate many forecast possibilities more quickly than other AI models.
Open → 2609.12815v1