用数字孪生技术提前测试超算调度策略的能耗与冷却影响。
HPC Digital Twins for Evaluating Scheduling Policies, Incentive Structures and their Impact on Power and Cooling
- 构建首个融合调度功能的超算数字孪生框架
- 支持在部署前模拟不同调度策略对能耗和冷却的影响
- 适合研究可持续计算与智能调度的科研人员
调度器在高性能计算（HPC）中对资源利用率至关重要。传统评估方法局限于部署后分析或不包含基础设施的仿真器。本文首次将调度与数字孪生技术结合于HPC领域，实现部署前的“假设性”分析，评估参数配置与调度决策对物理资产的影响，尤其适用于生产环境中难以实现的改动。我们（1）提出首个具备调度能力的数字孪生框架；（2）集成多个公开数据集中的顶尖HPC系统；（3）扩展支持外部调度仿真器接入。最终，我们（4）实现并评估激励机制，（5）在新型数字孪生元框架中测试基于机器学习的调度算法，用于原型验证。该工作使超算系统的可持续性评估和影响分析成为可能。
原文摘要 · Abstract (English)
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.
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