Accelerating dissipative particle dynamics simulations for soft matter systems
Accelerating dissipative particle dynamics simulations for soft matter systems
复制标题
DOI：
10.1016/j.commatsci.2014.10.068
复制
发表时间：
2015-04
影响因子：
3.3
通讯作者：
T. D. Nguyen;S. Plimpton
中科院分区：
文献类型：
--
作者：
T. D. Nguyen;S. Plimpton
耗散粒子动力学（DPD）是一种基于粗粒度粒子的模拟方法，提供了微观尺度的软物质系统的见解。我们提出了图形处理单元（gpu）的DPD模型的有效实现。由于在LAMMPS分子动力学包中实现，它可以在当前一代超级计算机上有效运行，这些超级计算机通常具有包含多核cpu和（一个或多个）gpu的混合节点。利用CPU和GPU之间有效的信息通信，DPD交互可以在GPU上计算，而完整仿真模型的其他部分（边界条件、约束、绑定交互、诊断计算等）可以在CPU上执行。与多核CPU模拟相比，我们的gpu增强运行显示出高达9.5倍的加速，并且可以在数千个计算节点上可扩展地运行。我们简要讨论了新的GPU实现如何针对CPU版本进行热力学、扩散和流体动力学行为的验证。我们还强调了更快的DPD实现所实现的大规模模型，用于单层自组装和薄膜不稳定性的研究。
Dissipative particle dynamics (DPD) is a coarse-grained particle-based simulation method that offers microscopic-scale insights into soft matter systems. We present an efficient implementation of a DPD model for graphical processing units (GPUs). As implemented in the LAMMPS molecular dynamics package, it can run effectively on current-generation supercomputers which often have hybrid nodes containing multi-core CPUs and (one or more) GPUs. Using efficient communication of information between the CPUs and GPUs, DPD interactions can be computed on the GPU while other portions of a full simulation model (boundary conditions, constraints, bonded interactions, diagnostic calculations, etc.) can be performed on the CPU. Our GPU-enhanced runs show a speedup of up to 9.5× versus many-core CPU simulations, and can run scalably across thousands of compute nodes. We briefly discuss how the new GPU implementation was validated against the CPU version for thermodynamics, diffusion, and hydrodynamic behavior. We also highlight large-scale models which the faster DPD implementation has enabled, for studies of monolayer self-assembly and thin-film instabilities.