发表机构
Cadence Design Systems（楷登电子）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对LLM带来的数据中心热管理挑战，提出AI驱动的热感知容量规划框架，实现秒级规划、毫秒级预测，较CFD加速10000倍，并优化负载与冷却效率。
AI 中文摘要
大型语言模型（LLMs）的出现对数据中心的热管理提出了重大挑战。LLMs的密集GPU计算会导致局部热点。此外，训练和推理突发期间的热负荷激增使得实时冷却响应更难预测和控制。数据中心的热感知容量规划需要大量昂贵的高保真CFD模拟。AI模型可以对未见过的设计进行实时预测。然而，现有工作要么预测误差大，要么对数据中心运营有过简化的假设。本工作提出了一个AI驱动的框架，能够在数秒内对真实世界的数据中心进行热感知容量规划。嵌入的AI模型从众多关键参数（机架功率、服务器功率、服务器放置、HVAC设置等）中学习，并在毫秒内提供温度预测。该AI模型针对高保真CFD模拟进行了测试，结果表明对于未见过的数据中心设计，模型能够实现高精度，并具有10000倍的加速。在AI模型的驱动下，作者设计了热感知容量规划框架。该框架可以帮助数据中心设计者和运营者即时优化工作负载分布和HVAC冷却效率。
英文摘要
The emerging of large language models (LLMs) has posed significant challenges to the thermal management of data center. Intense GPU computation for LLMs results in localized hotspots. Moreover, spiking thermal loads during training and inference bursts make real-time cooling response more difficult to predict and control. Thermal-aware capacity planning of data center requires massive expensive high-fidelity CFD simulations. AI models can perform real-time prediction for unseen designs. However, existing works either have large prediction error, or have over-simplified assumptions for data center operations. This work presents an AI-driven framework that can perform thermal-aware capacity planning for a real-world data center in seconds. The embedded AI model learns from numerous key parameters (rack power, server power, server placement, HVAC settings etc.), and provides temperature prediction within milliseconds. This AI model is tested against high-fidelity CFD simulations, and results show that for unseen data center designs, model can achieve high accuracy with 10000X speedup. Driven by the AI model, the authors design the thermal-aware capacity planning framework. This framework can help data center designers and operators instantaneously optimize both workload distribution and HVAC cooling efficiency.
CommentsPresented at DesignCon 2026
