A Digital Twin Framework for Liquid-cooled Supercomputers as Demonstrated at Exascale
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arXiv
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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913576314208256 |
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| author | Brewer, Wesley Maiterth, Matthias Kumar, Vineet Wojda, Rafal Bouknight, Sedrick Hines, Jesse Shin, Woong Greenwood, Scott Grant, David Williams, Wesley Wang, Feiyi |
| author_facet | Brewer, Wesley Maiterth, Matthias Kumar, Vineet Wojda, Rafal Bouknight, Sedrick Hines, Jesse Shin, Woong Greenwood, Scott Grant, David Williams, Wesley Wang, Feiyi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_05133 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Digital Twin Framework for Liquid-cooled Supercomputers as Demonstrated at Exascale Brewer, Wesley Maiterth, Matthias Kumar, Vineet Wojda, Rafal Bouknight, Sedrick Hines, Jesse Shin, Woong Greenwood, Scott Grant, David Williams, Wesley Wang, Feiyi Distributed, Parallel, and Cluster Computing Machine Learning 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. |
| title | A Digital Twin Framework for Liquid-cooled Supercomputers as Demonstrated at Exascale |
| topic | Distributed, Parallel, and Cluster Computing Machine Learning |
| url | https://arxiv.org/abs/2410.05133 |