SeT-Diff: Towards Semantic Foundation Models for HPC Telemetry and Time-Series
Provides a flexible digital twin for HPC systems, addressing the bottleneck of task-specific models that fail when sensor metrics vary.
SeT-Diff introduces the first foundational model for HPC telemetry, using diffusion conditioned on sensor descriptions to handle variable sensor configurations. It achieves 0.047 MAE on reconstruction and 0.033 MAE on thermal inference, enabling zero-shot permutation stability and multi-task capabilities.
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.