将从头算分子动力学的极限扩展到100亿个原子
Extending the limit of molecular dynamics with ab initio accuracy to 10 billion atoms
摘要：
高性能计算，以及从第一性原理方法生成的数据训练的神经网络模型，极大地推动了\textit{ab initio}分子动力学在现代超级计算机上的时空尺度上的应用。此前最先进的可以在整个Summit超级计算机上实现每天1亿个原子的$1-2$纳秒级分子动力学模拟。在本文中，我们已经显着减少了内存占用和计算时间的算法和系统创新的综合方法。神经网络模型通过模型列表、核融合和冗余去除进行压缩。在GPU和ARM架构上实现了定制内核加速、激活函数列表、MPI+OpenMP并行化等优化。铜系统的测试结果表明，优化后的代码可以扩展到Fugaku和Summit的整个机器，相应的系统大小可以扩展$134$的一个因素，以前所未有的$17$十亿原子。$13.5$ -百万原子铜系统的强缩放显示，溶液的时间可以快7倍，达到每天$11.2$纳秒。这项工作打开了大门，前所未有的大规模分子动力学模拟的基础上{ \it从头计算}的准确性，并可以潜在地利用在研究更现实的应用，如金属，半导体器件，电池等的机械性能的优化技术在本文中详细介绍的还提供了相关的高性能计算应用的见解。
英文摘要：
High-performance computing, together with a neural network model trained from data generated with first-principles methods, has greatly boosted applications of \textit{ab initio} molecular dynamics in terms of spatial and temporal scales on modern supercomputers. Previous state-of-the-art can achieve $1-2$ nanoseconds molecular dynamics simulation per day for 100-million atoms on the entire Summit supercomputer. In this paper, we have significantly reduced the memory footprint and computational time by a comprehensive approach with both algorithmic and system innovations. The neural network model is compressed by model tabulation, kernel fusion, and redundancy removal. Then optimizations such as acceleration of customized kernel, tabulation of activation function, MPI+OpenMP parallelization are implemented on GPU and ARM architectures. Testing results of the copper system show that the optimized code can scale up to the entire machine of both Fugaku and Summit, and the corresponding system size can be extended by a factor of $134$ to an unprecedented $17$ billion atoms. The strong scaling of a $13.5$-million atom copper system shows that the time-to-solution can be 7 times faster, reaching $11.2$ nanoseconds per day. This work opens the door for unprecedentedly large-scale molecular dynamics simulations based on {\it ab initio} accuracy and can be potentially utilized in studying more realistic applications such as mechanical properties of metals, semiconductor devices, batteries, etc. The optimization techniques detailed in this paper also provide insight for relevant high-performance computing applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
