{"ID":2868317,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.16513","arxiv_id":"2509.16513","title":"Trace Replay Simulation of MIT SuperCloud for Studying Optimal Sustainability Policies","abstract":"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.","short_abstract":"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 fra...","url_abs":"https://arxiv.org/abs/2509.16513","url_pdf":"https://arxiv.org/pdf/2509.16513v1","authors":"[\"Wesley Brewer\",\"Matthias Maiterth\",\"Damien Fay\"]","published":"2025-09-20T03:20:37Z","proceeding":"cs.DC","tasks":"[\"cs.DC\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}