Nonlinear time-history evolution problems employing high-fidelity physical models are essential in numerous scientific domains. However, these problems face a critical dual bottleneck: the immense computational cost of time-stepping and the massive memory requirements for maintaining a vast array of state variables. To address these challenges, we propose a novel framework based on heterogeneous memory management for massive ensemble simulations of general nonlinear time-history problems with complex constitutive laws. Taking advantage of recent advancements in CPU-GPU interconnect bandwidth, our approach actively leverages the large capacity of host CPU memory while simultaneously maximizing the throughput of the GPU. This strategy effectively overcomes the GPU memory wall, enabling memory-intensive simulations. We evaluate the performance of the proposed method through comparisons with conventional implementations, demonstrating significant improvements in time-to-solution and energy-to-solution. Furthermore, we demonstrate the practical utility of this framework by developing a Neural Network-based surrogate model using the generated massive datasets. The results highlight the effectiveness of our approach in enabling high-fidelity 3D evaluations and its potential for broader applications in data-driven scientific discovery.

翻译：采用高保真物理模型的非线性时程演化问题在众多科学领域中至关重要。然而，这类问题面临双重关键瓶颈：时间步进带来的巨大计算开销，以及维持海量状态变量所需的大量内存。为解决上述挑战，我们提出了一种基于异构内存管理的新框架，用于一般性含复杂本构关系非线性时程问题的大规模集成模拟。利用CPU-GPU互连带宽的最新进展，我们的方法在最大化GPU吞吐量的同时，主动利用主机CPU内存的大容量优势。该策略有效突破了GPU内存墙的限制，使得内存密集型模拟成为可能。通过与常规实现的对比，我们评估了所提方法的性能，结果表明其在求解时间和能量消耗方面均有显著改进。此外，我们利用生成的大规模数据集开发了基于神经网络的代理模型，展示了该框架的实际应用价值。结果凸显了我们的方法在实现高保真三维评估方面的有效性，及其在数据驱动科学发现中更广泛应用的潜力。