GPU-accelerated artificial neural network potential for molecular dynamics simulation
GPU-accelerated artificial neural network potential for molecular dynamics simulation
复制标题
GPU 加速的人工神经网络在分子动力学模拟方面的潜力
DOI：
10.1016/j.cpc.2022.108655
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Junya Inoue
中科院分区：
文献类型：
--
作者：
Meng Zhang;Koki Hibi;Junya Inoue
人工神经网络电位（Artificial neural network potential， ANNP）是通过第一性原理计算对大型数据库进行训练而得到的，由于它能够准确地捕捉分子动力学（MD）的物理和化学性质，在分子动力学（MD）模拟中得到了广泛的应用。然而，在实现过程中，复杂的过程和严重的数据依赖性使得CPU-only的运行性能变差，进一步限制了它的应用。在这篇文章中，我们报告了一种灵活的LAMMPS中ANNP的计算方法，该方法根据加速器上的资源（如全局内存的大小和工作项（核心）的数量）将仿真箱分成几个部分。当每个循环计算的原子数大于设备上的工作项数时，划分部件的数量对性能的影响很小。在这种方法中，使用分层内存更新相邻原子的力，而不需要原子操作。执行典型的动态和静态测试来验证实现。结果表明，当使用一个图形处理单元（GPU）时，我们的方法比仅运行8 mpi任务的cpu快12或13倍。此外，支持CUDA和opencl的GPU卡也支持此实现。
Artificial neural network potential (ANNP), obtained by training a large database by the first-principles calculations, has become popular in molecular dynamics (MD) simulation since it can capture accurate physical and chemical properties. However, the complex procedure and heavy data dependence during implementation make the performance of CPU-only runs worse, which further limits its application. In this contribution, we report a flexible computation method for ANNP in LAMMPS, in which the simulation box is divided into several parts in accordance with the resource on the accelerator such as the size of global memory and the number of work items (cores). The number of dividing parts has little influence on the performance when the number of calculated atoms per loop is larger than the number of work items on the device. In this approach, the forces of neighbor atoms are updated using hierarchical memory without atomic operation. Typicaldynamicandstatictests are performed to validate the implementation. The results show that our approach is 12 or 13 times faster when using one graphics processing unit (GPU) compared with 8-MPI tasks CPU-only runs. Additionally, this implementation is supported for CUDA- and OpenCL-enabled GPU cards.
影响因子：
5.5
作者：
Doerr S;Majewski M;Pérez A;Krämer A;Clementi C;Noe F;Giorgino T;De Fabritiis G
通讯作者：
De Fabritiis G
影响因子：
2.9
作者：
Montavon, Gregoire;Samek, Wojciech;Mueller, Klaus-Robert
通讯作者：
Mueller, Klaus-Robert