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

AI 中文解读

数据中心和超级计算机的“数字孪生”一直是运维难点——过去模型就像定制西装，换个任务或传感器就得重做。现在，科学家们提出了SeT-Diff，首款能“读懂”传感器名字的通用模型。它不依赖固定传感器位置，而是根据每个传感器的“身份标签”自动生成数据，哪怕传感器编号打乱也能准确工作。在真实超级计算机测试中，它重建数据的误差仅4.7%，还能零样本完成填补缺失值、预测未来趋势甚至推断温度（热误差仅3.3%）。这意味着，一个预训练模型就能应对各种监控任务，屏蔽了硬件更换或布局调整带来的头痛。对普通人来说，这项技术将直接提升数据中心的能源效率和冷却成本，比如你使用的云服务、AI训练任务会更稳定、更省电，未来智能城市、天气预测这类需要海量传感器协同的系统也会变得更聪明可靠。

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.