SeT-Diff: Towards Semantic Foundation Models for HPC Telemetry and Time-Series

Researchers have introduced SeT-Diff, the first foundational model for high-performance computing (HPC) node telemetry and time-series data, achieving a Mean Absolute Error (MAE) of 0.0470 on reconstruction tasks. The diffusion-based model conditions generation on sensor semantic descriptions, enabling zero-shot permutation stability and multi-task capabilities including data imputation, forecasting, and virtual sensing with a 0.033 MAE in thermal inference. This work, detailed in arXiv:2607.22548v1, aims to create flexible digital twins for data centers that remain effective when sensor configurations or tasks change.

arXiv:2607.22548v1 Announce Type: new Abstract: Data centers and their compute nodes require accurate and flexible digital twins capable of modeling the complex interplay of workloads, environmental parameters, and physical metrics. Current machine learning approaches for HPC and its telemetry typically rely on a static subset of anonymous, fixed-position sensor variables tailored to single tasks. Consequently, these models become obsolete when target tasks change or sensor metrics vary. We propose SeT-Diff, the first foundational model for compute node telemetry and time-series. Unlike rigid architectures, our diffusion-based approach conditions the generative process on each sensor's semantic description, decoupling the system dynamics from the structure of the dataset. Experiments on a real-world supercomputer dataset demonstrate a Mean Absolute Error MAE of 0.0470 on reconstruction tasks. SeT-Diff exhibits zero-shot permutation stability, maintaining accuracy with negligible degradation even when sensors are shuffled. A single pre-trained model effectively performs data imputation, forecasting, and virtual sensing - achieving a 0.033 MAE in thermal inference - making SeT-Diff an effective data-driven digital twin for HPC systems.