mlsanityData to 7 Oct 2026

A Digital Twin Framework for Liquid-cooled Supercomputers as Demonstrated at Exascale

arXiv 2410.05133 ↗First posted 7 Oct 2024cs.DC cs.LGcited by 3

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Abstract (arXiv)

We present ExaDigiT, an open-source framework for developing comprehensive digital twins of liquid-cooled supercomputers. It integrates three main modules: (1) a resource allocator and power simulator, (2) a transient thermo-fluidic cooling model, and (3) an augmented reality model of the supercomputer and central energy plant. The framework enables the study of "what-if" scenarios, system optimizations, and virtual prototyping of future systems. Using Frontier as a case study, we demonstrate the framework's capabilities by replaying six months of system telemetry for systematic verification and validation. Such a comprehensive analysis of a liquid-cooled exascale supercomputer is the first of its kind. ExaDigiT elucidates complex transient cooling system dynamics, runs synthetic or real workloads, and predicts energy losses due to rectification and voltage conversion. Throughout our paper, we present lessons learned to benefit HPC practitioners developing similar digital twins. We envision the digital twin will be a key enabler for sustainable, energy-efficient supercomputing.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 7 of its 75 references that are in the map. First posted on arXiv 7 Oct 2024.

Builds on

7 of 75 references.

References · 7

  1. 2019Machine Learning for Fluid Mechanicscited by 380
  2. 2020The gem5 Simulator: Version 20.0+cited by 13
  3. 2020An In-Depth Analysis of the Slingshot Interconnectcited by 13
  4. 2023Toward Sustainable HPC: Carbon Footprint Estimation and Environmental Implications of HPC Systemscited by 12
  5. 2021Deep Reinforcement Agent for Scheduling in HPCcited by 10
  6. 2021Composing Modeling and Simulation with Machine Learning in Juliacited by 3
  7. 20213D Real-Time Supercomputer Monitoringcited by 1

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Citing papers per year: 2025: 3.

3 papers.

  1. 2025HPC Digital Twins for Evaluating Scheduling Policies, Incentive Structures and their Impact on Power and Coolingcited by 3
  2. 2025LC-Opt: Benchmarking Reinforcement Learning and Agentic AI for End-to-End Liquid Cooling Optimization in Data Centerscited by 3
  3. 2025AI Benchmark Democratization and Carpentrycited by 3