Polymer simulations are among the most computationally demanding workloads in soft-matter research, often requiring days of execution and high energy consumption to achieve physically meaningful results. In this work, we address these challenges through the coupling and optimization of two complementary simulation frameworks: the Uneyama-Doi Model (UDM) and the SOft coarse-grained Monte Carlo Acceleration (SOMA). UDM efficiently propagates concentration fields at the continuum level, while SOMA resolves chain-scale thermal fluctuations via particle-based Monte Carlo dynamics. Each model was individually optimized for GPU execution using kernel fusion, memory coalescing, asynchronous random-number generation yielding up to 70% (UDM) and 80% (SOMA) performance improvement. The coupling is performed through our proposed coordinator library that orchestrates data exchange and synchronizes time-stepping across multiple GPUs. Further management of coupling workload distribution enabled a 13x overall speedup and 24.5x reduction in total energy usage compared to the SOMA baseline, i. e., 96% energy saving. The proposed hybrid approach maintains the same scientific fidelity while drastically reducing the computational and energy footprint, showcasing the potential of energy-aware, cross-application co-design for sustainable high-performance simulations

翻译：高分子模拟是软物质研究中计算需求最苛刻的任务之一，通常需要数天执行时间和高能耗才能获得具有物理意义的结果。本研究通过耦合与优化两种互补模拟框架——Uneyama-Doi模型（UDM）与软粗粒蒙特卡洛加速（SOMA）——应对这些挑战。UDM在连续介质层面高效传播浓度场，而SOMA通过基于粒子的蒙特卡洛动力学解析链尺度热涨落。通过使用核融合、内存合并、异步随机数生成技术，各模型单独针对GPU执行优化后，分别实现了UDM性能提升达70%、SOMA达80%。我们提出的协调器库实现了耦合过程，该库可编排多GPU间数据交换并同步时间步长。进一步对耦合工作负载分布进行管理后，与SOMA基线相比实现了13倍整体加速和24.5倍总能耗降低（即节能96%）。所提出的混合方法在保持相同科学精度的同时大幅降低了计算与能量足迹，展示了面向可持续高性能计算的跨应用能耗感知协同设计潜力。