2026-07-28 04:00 UTCOriginal source2 min readUpdated: 2026-07-28 08:22 UTC

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

SeT-Diff is the first foundational model for compute node telemetry and time-series. Using a diffusion-based approach conditioned on semantic descriptions of sensors, it decouples system dynamics from dataset structure. Experiments on a real supercomputer show a reconstruction MAE of 0.047 and thermal inference MAE of 0.033, with zero-shot permutation stability.

SourcearXiv AIAuthor: Giovanni B. Esposito, Francesco Antici, Daniele Cesarini, Andrea Bartolini

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[Submitted on 11 May 2026]

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

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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.

Subjects:

Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Performance (cs.PF)

Cite as: arXiv:2607.22548 [cs.AI]

(or arXiv:2607.22548v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.22548

arXiv-issued DOI via DataCite

Submission history

From: Giovanni B. Esposito [view email] [v1] Mon, 11 May 2026 00:43:50 UTC (105 KB)

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