HPC Digital Twins for Evaluating Scheduling Policies, Incentive Structures and their Impact on Power and Cooling
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915530305175552 |
|---|---|
| author | Maiterth, Matthias Brewer, Wesley H. Kuruvella, Jaya S. Dey, Arunavo Islam, Tanzima Z. Menear, Kevin Duplyakin, Dmitry Kabir, Rashadul Patki, Tapasya Jones, Terry Wang, Feiyi |
| author_facet | Maiterth, Matthias Brewer, Wesley H. Kuruvella, Jaya S. Dey, Arunavo Islam, Tanzima Z. Menear, Kevin Duplyakin, Dmitry Kabir, Rashadul Patki, Tapasya Jones, Terry Wang, Feiyi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_20016 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HPC Digital Twins for Evaluating Scheduling Policies, Incentive Structures and their Impact on Power and Cooling Maiterth, Matthias Brewer, Wesley H. Kuruvella, Jaya S. Dey, Arunavo Islam, Tanzima Z. Menear, Kevin Duplyakin, Dmitry Kabir, Rashadul Patki, Tapasya Jones, Terry Wang, Feiyi Distributed, Parallel, and Cluster Computing Artificial Intelligence Emerging Technologies Systems and Control 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. |
| title | HPC Digital Twins for Evaluating Scheduling Policies, Incentive Structures and their Impact on Power and Cooling |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence Emerging Technologies Systems and Control |
| url | https://arxiv.org/abs/2508.20016 |