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

arXiv: https://arxiv.org/abs/2410.05133 · HTML: https://mlsanity.com/paper/2410.05133/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 75 references that are in the map.
- First posted on arXiv 7 Oct 2024.

## Builds on (most cited first, 7 of 7)

- [Machine Learning for Fluid Mechanics](https://mlsanity.com/paper/1905.11075/) (2019)
- [The gem5 Simulator: Version 20.0+](https://mlsanity.com/paper/2007.03152/) (2020)
- [An In-Depth Analysis of the Slingshot Interconnect](https://mlsanity.com/paper/2008.08886/) (2020)
- [Toward Sustainable HPC: Carbon Footprint Estimation and Environmental Implications of HPC Systems](https://mlsanity.com/paper/2306.13177/) (2023)
- [Deep Reinforcement Agent for Scheduling in HPC](https://mlsanity.com/paper/2102.06243/) (2021)
- [Composing Modeling and Simulation with Machine Learning in Julia](https://mlsanity.com/paper/2105.05946/) (2021)
- [3D Real-Time Supercomputer Monitoring](https://mlsanity.com/paper/2109.04532/) (2021)

## Built on by (most cited first, 3 of 3)

- [HPC Digital Twins for Evaluating Scheduling Policies, Incentive Structures and their Impact on Power and Cooling](https://mlsanity.com/paper/2508.20016/) (2025)
- [LC-Opt: Benchmarking Reinforcement Learning and Agentic AI for End-to-End Liquid Cooling Optimization in Data Centers](https://mlsanity.com/paper/2511.00116/) (2025)
- [AI Benchmark Democratization and Carpentry](https://mlsanity.com/paper/2512.11588/) (2025)

## 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.