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HPC-AI融合下磁约束聚变集成建模系统中的可追溯性研究OA

From Building to Packaging:A Study on FAIR Data Traceability in HPC:AI-Driven Integrated Modeling of Magnetic Confinement Fusion

中文摘要英文摘要

[背景]随着磁约束聚变研究深入与计算能力提升,聚变集成建模正向多物理、多尺度耦合系统演进.人工智能技术的广泛应用催生了HPC-AI混合计算范式.[目的]然而,传统HPC程序依赖稳定编译环境,AI应用依赖动态Python生态与容器化技术,二者在依赖管理、构建方式和运行模式上的差异,导致现有集成建模框架(FuYun)难以统一管理异构组件,造成计算结果在HPC-AI边界处追溯断裂,威胁可重复性与可追溯性.本研究旨在解决HPC-AI融合环境下FuYun中软件环境管理及物理模块运行面临的FAIR可行性问题.[方法]扩展现有FuYun系统,将管理范围从HPC"构建"延伸至AI"封装",引入"计算单元"统一抽象层,将HPC程序与容器化AI应用纳入同一实体;设计跨技术栈的统一标识符(@pid)体系与追溯机制,并在模块描述中新增AI元数据字段(如模型权重、训练参数、随机种子等).[结果]扩展后框架实现了异构组件在同一个任务流程中统一运行和管理,计算环境可重现率达95%,环境部署效率较手动方式提升85%.该方法缩小了用户在集成系统内同时使用HPC模块和AI模块的鸿沟,用户可以灵活选择不同计算模块组织成分析工作流,并可以对计算过程和结果进行记录.

[Background]With advances in magnetic confinement fusion research and computing capabilities,integrated fu-sion modeling is evolving from single-physics modules toward complex,multi-physics,multi-scale coupled sys-tems.The widespread adoption of artificial intelligence(AI)has led to an HPC-AI hybrid computing paradigm.[Objective]However,traditional HPC codes rely on stable system-level compilation environments,while AI ap-plications depend on dynamic Python ecosystems and containerization.Their fundamental differences in depen-dency management,build processes,and execution models make it difficult for the existing in-house integrated modeling framework(FuYun)to uniformly manage heterogeneous components,causing traceability gaps at the HPC-AI interface and threatening reproducibility and provenance.This study aims to address FAIR compliance challenges in software environment management and physics module execution within FuYun under HPC-AI inte-grated environments.[Methods]This study extends FuYun's scope from HPC"build"to AI"packaging,"intro-ducing a unified abstraction called the Computational Unit(CU)to encapsulate both traditional HPC programs and containerized AI applications.A cross-stack unique identifier(@pid)system and provenance tracking mecha-nism are designed.The module description schema is enhanced with AI-specific metadata fields(e.g.,model weights,training hyperparameters,random seeds)to ensure complete recording of critical information.[Results]The extend-ed framework successfully unifies management of heterogeneous components.Full-stack data provenance is achieved via the@pid system and enhanced tracking.Experiments show a 95%reproducibility rate and an 85%improvement in environment deployment efficiency over manual methods.This approach bridges the gap be-tween HPC modules and AI modules,allowing users to flexibly select different computational modules and orga-nize them into analysis workflows within an integrated platform,and to record the compute flow and result.

刘晓娟;于治;张运动

中国科学院合肥物质科学研究院等离子体物理研究所,安徽 合肥 230031中国科学院合肥物质科学研究院等离子体物理研究所,安徽 合肥 230031中国科学技术大学网络信息中心,安徽 合肥 230026

数据管理FAIR4RS集成建模容器磁约束聚变可追溯性

data managementFAIR4RSintegrated modelingcontainersmagnetic confinement fusionprovenance

《数据与计算发展前沿》 2026 (3)

15-28,14

国家自然科学基金(12575217,12575272,12505263)国家重点研发计划(2024YFE03050002)

10.11871/jfdc.issn.2096-742X.2026.03.002

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