从空闲到紧急：一种资源收割的高保真地震估计高性能计算工作流
From Idle to Urgent: A Resource-Harvested HPC Workflow for High-Fidelity Seismic Estimation
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中文总结 AI 辅助
提出一种紧急交互式HPC工作流，结合资源收割与神经网络代理，动态集成高保真3D非线性分析，实现地震期间快速决策，降低能耗76%、吞吐量提升3.7倍，紧急时计算成本降低97.9%以上，30分钟内生成可靠地面运动分布。
中文摘要 AI 辅助
我们提出了一种紧急交互式高性能计算工作流，该工作流动态集成高保真三维非线性分析与代理神经网络（NN），以在大规模地震期间实现快速决策。该方法实现了“资源收割”，即在非紧急时期利用空闲计算能力，以及在危机期间即时响应。通过开发两个专门的高性能计算内核，所提方法在正常运行期间将能量消耗降低76%，吞吐量提高3.7倍，以高效构建训练数据集；而在紧急情况下，它将基于神经网络的逆分析与基于物理的模拟及动态细化相结合，将传统计算成本降低超过97.9%，从而在地震后30分钟内生成高度可靠的空间时程地面运动分布。
英文摘要
We propose an Urgent Interactive HPC workflow that dynamically integrates high-fidelity 3D nonlinear analysis with surrogate neural networks (NNs) to enable rapid decision-making during large-scale earthquakes. This approach achieves both "Resource Harvesting", which utilizes idle computing capacity during non-emergency periods, and immediate response during crises. By developing two specialized HPC kernels, the proposed method reduces energy-to-solution by 76% and improves throughput by 3.7-fold during normal operations to efficiently construct training datasets, while during emergencies, it couples NN-based inverse analysis with physics-based simulations and dynamic refinement to reduce conventional computational costs by over 97.9%, enabling the generation of highly reliable spatial time-history ground motion distributions within 30 minutes post-earthquake.
发表机构
- The University of Tokyo（东京大学）
- RIKEN Center for Computational Science（理化学研究所计算科学中心）
- Japan Agency for Marine-Earth Science and Technology（日本海洋地球科学技术机构）
机构由 AI 辅助整理，请以论文原文为准。
