# HPC Digital Twins for Evaluating Scheduling Policies, Incentive Structures and their Impact on Power and Cooling

arXiv: https://arxiv.org/abs/2508.20016 · HTML: https://mlsanity.com/paper/2508.20016/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 49 references that are in the map.
- First posted on arXiv 27 Aug 2025.

## Builds on (most cited first, 2 of 2)

- [Scheduling Beyond CPUs for HPC](https://mlsanity.com/paper/2012.05439/) (2020)
- [A Digital Twin Framework for Liquid-cooled Supercomputers as Demonstrated at Exascale](https://mlsanity.com/paper/2410.05133/) (2024)

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

- [AI Benchmark Democratization and Carpentry](https://mlsanity.com/paper/2512.11588/) (2025)
- [SeT-Diff: Towards Semantic Foundation Models for HPC Telemetry and Time-Series](https://mlsanity.com/paper/2607.22548/) (2026)
- [Leakage-Safe and Scheduler-Aware Machine Learning for Grid Job Runtime Prediction](https://mlsanity.com/paper/2609.13701/) (2026)

## Abstract (arXiv)

Schedulers are critical for optimal resource utilization in high-performance computing. Traditional methods to evaluate schedulers are limited to post-deployment analysis, or simulators, which do not model associated infrastructure. In this work, we present the first-of-its-kind integration of scheduling and digital twins in HPC. This enables what-if studies to understand the impact of parameter configurations and scheduling decisions on the physical assets, even before deployment, or regarching changes not easily realizable in production. We (1) provide the first digital twin framework extended with scheduling capabilities, (2) integrate various top-tier HPC systems given their publicly available datasets, (3) implement extensions to integrate external scheduling simulators. Finally, we show how to (4) implement and evaluate incentive structures, as-well-as (5) evaluate machine learning based scheduling, in such novel digital-twin based meta-framework to prototype scheduling. Our work enables what-if scenarios of HPC systems to evaluate sustainability, and the impact on the simulated system.