超级计算机的数字孪生对象模型分析

中文摘要 AI 辅助

操作员和开发者需要同时理解大型超级计算机的结构和实时行为的心智模型，但其物理布局、逻辑组织以及每节点遥测数据流难以相互关联，导致难以将某个指标或事件追溯到具体的硬件组件。我们提出了DAT，一个基于实时游戏引擎Unreal Engine构建的计算集群交互式三维数字分析孪生。DAT将超级计算机的紧凑参数化描述（即可复用的数字孪生原型DTP）扩展为可导航的数字孪生实例DTI，该实例镜像了机架、机箱、刀片和网络链路的物理包含层级，并将每个节点的角色和健康状态编码在其外观中，同时一个轻量级事件驱动模拟器在虚拟时钟上动画展示作业和硬件活动。我们当前的实现增加了一个双路径节点选择机制，将直接三维指向与命令行查询统一起来，在任何选定组件旁打开一个世界内可视化分析面板，显示汇总统计和实时时变指标。我们描述了这一架构，报告了工作原型中的定性行为，并概述了使用记录遥测数据和原位异常检测驱动面板的路径。

英文摘要

Operators and developers need a mental model of both the structure and the live behavior of a large supercomputer, but its physical layout, logical organization, and streams of per-node telemetry are difficult to relate to one another, making it hard to trace a metric or event back to a specific hardware component. We present DAT, an interactive three-dimensional digital analytics twin of a compute cluster built in a real-time game engine, Unreal Engine. DAT expands a compact, parametric description of a supercomputer, a reusable Digital Twin Prototype (DTP), into a navigable Digital Twin Instance (DTI) that mirrors its physical containment hierarchy of racks, chassis, blades, and network links, encoding each node's role and health in its appearance, while a lightweight event-driven simulator animates job and hardware activity over a virtual clock. Our current implementation adds a two-path node-selection mechanism, unifying direct 3D pointing with command-shell queries, that opens an in-world visual-analytics panel beside any selected component showing summary statistics and live, time-varying metrics. We describe this architecture, report qualitative behavior from the working prototype, and outline the path toward driving the panels with recorded telemetry and in-situ anomaly detection.

发表机构

• Leadership Computing Facility, Argonne National Laboratory（领导力计算设施，阿贡国家实验室）
• Department of Computer Science, Loyola University Chicago（计算机科学系，芝加哥洛约拉大学）
• Department of Computer Science, University of Illinois at Chicago（计算机科学系，伊利诺伊大学芝加哥分校）

机构由 AI 辅助整理，请以论文原文为准。