Digital twins are transforming the way we monitor, analyze, and control physical systems, but designing architectures that balance real-time responsiveness with heavy computational demands remains a challenge. Cloud-based solutions often struggle with latency and resource constraints, while edge-based approaches lack the processing power for complex simulations and data-driven optimizations. To address this problem, we propose the High-Precision High-Performance Computer-enabled Digital Twin (HP2C-DT) reference architecture, which integrates High-Performance Computing (HPC) into the computing continuum. Unlike traditional setups that use HPC only for offline simulations, HP2C-DT makes it an active part of digital twin workflows, dynamically assigning tasks to edge, cloud, or HPC resources based on urgency and computational needs. Furthermore, to bridge the gap between theory and practice, we introduce the HP2C-DT framework, a working implementation that uses COMPSs for seamless workload distribution across diverse infrastructures. We test it in a power grid use case, showing how it reduces communication bandwidth by an order of magnitude through edge-side data aggregation, improves response times by up to 2x via dynamic offloading, and maintains near-ideal strong scaling for compute-intensive workflows across a practical range of resources. These results demonstrate how an HPC-driven approach can push digital twins beyond their current limitations, making them smarter, faster, and more capable of handling real-world complexity.

翻译：数字孪生正在改变我们监测、分析和控制物理系统的方式，但设计一种能够平衡实时响应性与繁重计算需求的架构仍然是一个挑战。基于云的解决方案常常受限于延迟和资源约束，而基于边缘的方法则缺乏处理复杂仿真和数据驱动优化的计算能力。为解决这一问题，我们提出了高精度高性能计算赋能的数字孪生（HP2C-DT）参考架构，该架构将高性能计算（HPC）集成到计算连续体中。与传统仅将HPC用于离线仿真的设置不同，HP2C-DT使其成为数字孪生工作流的活跃组成部分，能够根据任务的紧急程度和计算需求，动态地将任务分配给边缘、云或HPC资源。此外，为弥合理论与实践之间的差距，我们引入了HP2C-DT框架，这是一个使用COMPSs在不同基础设施间实现无缝工作负载分发的实际实现。我们在一个电网用例中对其进行了测试，结果表明：通过边缘侧数据聚合，它将通信带宽降低了一个数量级；通过动态卸载，响应时间提升了高达2倍；并且在实践资源范围内，为计算密集型工作流保持了接近理想的强扩展性。这些结果证明了HPC驱动的方法如何能够推动数字孪生突破当前局限，使其更智能、更快速，并更能应对现实世界的复杂性。