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

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 supercompu...

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Vydané v:SC24: International Conference for High Performance Computing, Networking, Storage and Analysis s. 1 - 18
Hlavní autori: Brewer, Wesley, Maiterth, Matthias, Kumar, Vineet, Wojda, Rafal, Bouknight, Sedrick, Hines, Jesse, Shin, Woong, Greenwood, Scott, Grant, David, Williams, Wesley, Wang, Feiyi
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 17.11.2024
Predmet:
augmented reality
Cooling
data center power
Digital twins
electronics cooling
energy efficiency
exascale computing
Optimization
Supercomputers
System dynamics
Systematics
Telemetry
Transient analysis
Virtual prototyping
Voltage
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Shrnutí: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.
DOI:10.1109/SC41406.2024.00029