发表机构
Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich; Interdisciplinary Centre for Advanced Materials Simulations (ICAMS), Ruhr Universität Bochum（于利希超级计算中心，于利希研究中心; 鲁尔大学波鸿跨学科高级材料模拟中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对自适应精度原子间势模拟，比较LAMMPS与ALL库的负载均衡方法，ALL修正张量法将不平衡因子从3.0降至1.7，并提出理想负载均衡频率策略。
AI 中文摘要
对于原子级分子动力学模拟，我们考虑了一种最近开发的混合耦合方法，将高精度的基于机器学习（ML）的原子团簇展开（ACE）相互作用模型与精度较低（但速度快约1-2个数量级）的EAM势相结合，以解决纯ML势在并行计算环境中的性能瓶颈。这种时空自适应性有潜力实现超过一个数量级的加速，但如果没有动态负载均衡策略，这种加速将无法实现，而由于势贡献的波动，动态负载均衡变得至关重要。我们比较了LAMMPS的负载均衡策略与ALL库提供的负载均衡方法，在铜纳米压痕的自适应精度EAM-ACE模拟中进行了对比。对于规则网格的域分解，ALL库的修正张量方法（保留了张量方法的简单性和可移植性）将力计算的不平衡因子从3.0显著降低至1.7，相比LAMMPS的原生负载均衡方法。通过使用LAMMPS的递归二分法对不规则网格域进行负载均衡，以及使用ALL的直方图方法对交错网格域进行负载均衡，可以实现接近完美平衡的系统，不平衡因子接近1.0。研究发现，使用ALL对交错网格进行负载均衡比使用LAMMPS的二分法对不规则网格进行负载均衡快得多，但在常规时间步长内，不规则网格域的通信成本更低，因为其域的表面积与体积之比更低。此外，我们提出了一种策略，用于寻找自适应精度模拟的理想负载均衡频率。
英文摘要
For atomistic molecular dynamics simulations, we consider a recently developed hybrid coupling between the highly accurate machine learning (ML)-based atomic cluster expansion (ACE) interaction model and a less precise (but about 1-2 orders faster) EAM potential, in order to leverage the performance bottleneck of pure ML potentials in a parallel computing environment. This spatial-temporal adaptivity has the potential for a speedup of more than an order of magnitude, that would be lost without a dynamic load-balancing strategy that becomes critical due to fluctuating potential contributions. We compare the load-balancing strategies of LAMMPS with load-balancing methods provided by the library ALL in adaptive-precision EAM-ACE simulations of a copper nanoindentation. For a regular-grid domain decomposition, the modified tensor method of the ALL library, which conserves the simplicity and transferability of the tensor method, significantly reduces the imbalance factor of the force calculation from 3.0 to 1.7 compared to the native load-balancing method of LAMMPS. A nearly perfectly balanced system with an imbalance factor close to 1.0 can be reached by load balancing an irregular grid of domains with the recursive bisectioning method of LAMMPS and with ALL's histogram method for a staggered grid of domains. Load balancing a staggered grid with ALL is found to be significantly faster than load balancing an irregular grid with the bisectioning method of LAMMPS, but the communication costs during a regular timestep are lower for the irregular grid of domains, as a lower surface-by-volume ratio of the domains is reached. Furthermore, we suggest a strategy to find the ideal load-balancing frequency for adaptive-precision simulations.
CommentsPPAM 2026, 15 pages, 5 figures
