无动量跳变的混合量子-经典刘维尔分子动力学的GPU实现
GPU implementation of mixed quantum-classical Liouville molecular dynamics without momentum jump
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中文总结 AI 辅助
该研究在GPU上实现无动量跳变的混合量子-经典刘维尔分子动力学，移除CPU端轨迹生成以降低GPU开销，获数量级加速且采样轨迹数呈线性缩放。
中文摘要 AI 辅助
我们在GPU上实现了一种基于无动量跳变理论的混合量子-经典刘维尔分子动力学模拟。此前为增强采样在CPU上实现的轨迹生成被移除，以避免GPU上线程发散和动态内存分配的开销。与带轨迹生成的CPU计算相比，这实现了一个数量级的加速，且相对于采样轨迹数量呈线性缩放。
英文摘要
We implemented on GPU a mixed quantum-classical Liouville molecular dynamics simulation based on a momentum-jump-free theory. The trajectory spawning that was previously implemented on CPU for sampling enhancement was eliminated to avoid the overhead of thread divergence and dynamic memory allocation on the GPU. This achieved a speedup of an order of magnitude compared to the CPU computation with spawning, as well as a linear scaling with respect to the number of sampling trajectories.
