Trace Replay Simulation of MIT SuperCloud for Studying Optimal Sustainability Policies
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_ | 1866909798471041024 |
|---|---|
| author | Brewer, Wesley Maiterth, Matthias Fay, Damien |
| author_facet | Brewer, Wesley Maiterth, Matthias Fay, Damien |
| contents | The rapid growth of AI supercomputing is creating unprecedented power demands, with next-generation GPU datacenters requiring hundreds of megawatts and producing fast, large swings in consumption. To address the resulting challenges for utilities and system operators, we extend ExaDigiT, an open-source digital twin framework for modeling power, cooling, and scheduling of supercomputers. Originally developed for replaying traces from leadership-class HPC systems, ExaDigiT now incorporates heterogeneity, multi-tenancy, and cloud-scale workloads. In this work, we focus on trace replay and rescheduling of jobs on the MIT SuperCloud TX-GAIA system to enable reinforcement learning (RL)-based experimentation with sustainability policies. The RAPS module provides a simulation environment with detailed power and performance statistics, supporting the study of scheduling strategies, incentive structures, and hardware/software prototyping. Preliminary RL experiments using Proximal Policy Optimization demonstrate the feasibility of learning energy-aware scheduling decisions, highlighting ExaDigiT's potential as a platform for exploring optimal policies to improve throughput, efficiency, and sustainability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_16513 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Trace Replay Simulation of MIT SuperCloud for Studying Optimal Sustainability Policies Brewer, Wesley Maiterth, Matthias Fay, Damien Distributed, Parallel, and Cluster Computing The rapid growth of AI supercomputing is creating unprecedented power demands, with next-generation GPU datacenters requiring hundreds of megawatts and producing fast, large swings in consumption. To address the resulting challenges for utilities and system operators, we extend ExaDigiT, an open-source digital twin framework for modeling power, cooling, and scheduling of supercomputers. Originally developed for replaying traces from leadership-class HPC systems, ExaDigiT now incorporates heterogeneity, multi-tenancy, and cloud-scale workloads. In this work, we focus on trace replay and rescheduling of jobs on the MIT SuperCloud TX-GAIA system to enable reinforcement learning (RL)-based experimentation with sustainability policies. The RAPS module provides a simulation environment with detailed power and performance statistics, supporting the study of scheduling strategies, incentive structures, and hardware/software prototyping. Preliminary RL experiments using Proximal Policy Optimization demonstrate the feasibility of learning energy-aware scheduling decisions, highlighting ExaDigiT's potential as a platform for exploring optimal policies to improve throughput, efficiency, and sustainability. |
| title | Trace Replay Simulation of MIT SuperCloud for Studying Optimal Sustainability Policies |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2509.16513 |