The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Poster Type: Research Posters
Author: Yan Chen (Pacific Northwest National Laboratory (PNNL)), Xing Lu (Pacific Northwest National Laboratory (PNNL)), Cary Faulkner (Pacific Northwest National Laboratory (PNNL)), Alex Vlachokostas (Pacific Northwest National Laboratory (PNNL)), Hanlong Wan (Pacific Northwest National Laboratory (PNNL)), Jeremy Lerond (Pacific Northwest National Laboratory (PNNL))
Supervisor:
Abstract: High performance computing (HPC) workloads are driving rack power densities beyond 100 kW, creating unprecedented stress on data center cooling and power systems. Conventional CFD-based digital twins provide high-fidelity design optimization but are too computationally intensive and rigid for operational use. We present the first physics-constrained Distributed Modular Digital Twin Network (DMDTN), designed for real-time performance evaluation, load prediction, and fault detection. Each subsystem (e.g., cooling, power, IT load) is represented by an AI-driven surrogate model, interconnected through conservation laws and coordinated via a distributed message bus. This modular design preserves physical consistency while enabling scalability and rapid adaptability. Using synthetic datasets, DMDTN achieved ~60% lower prediction error (RMSE 172 vs. 450) and more than 2× faster training (201 vs. 442 seconds) than a monolithic model, while maintaining robustness under stress. DMDTN complements CFD by enabling accurate, real-time operational management of HPC data centers.
Best Poster Finalist (BP): no
Poster: PDF
Poster Summary: PDF