Martínez Enrique, Uberuaga Blas P, Voter Arthur F
Material Science and Technology Division, MST-8, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA.
Theoretical Division, T-1, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA.
Phys Rev E Stat Nonlin Soft Matter Phys. 2014 Jun;89(6):063308. doi: 10.1103/PhysRevE.89.063308. Epub 2014 Jun 18.
Exascale computing presents a challenge for the scientific community as new algorithms must be developed to take full advantage of the new computing paradigm. Atomistic simulation methods that offer full fidelity to the underlying potential, i.e., molecular dynamics (MD) and parallel replica dynamics, fail to use the whole machine speedup, leaving a region in time and sample size space that is unattainable with current algorithms. In this paper, we present an extension of the parallel replica dynamics algorithm [A. F. Voter, Phys. Rev. B 57, R13985 (1998)] by combining it with the synchronous sublattice approach of Shim and Amar [ and , Phys. Rev. B 71, 125432 (2005)], thereby exploiting event locality to improve the algorithm scalability. This algorithm is based on a domain decomposition in which events happen independently in different regions in the sample. We develop an analytical expression for the speedup given by this sublattice parallel replica dynamics algorithm and compare it with parallel MD and traditional parallel replica dynamics. We demonstrate how this algorithm, which introduces a slight additional approximation of event locality, enables the study of physical systems unreachable with traditional methodologies and promises to better utilize the resources of current high performance and future exascale computers.
百亿亿次计算给科学界带来了挑战，因为必须开发新算法以充分利用这种新的计算范式。能够完全忠实于基础势能的原子模拟方法，即分子动力学（MD）和平行副本动力学，无法充分利用整个机器的加速能力，从而在时间和样本量空间中留下了当前算法无法达到的区域。在本文中，我们通过将并行副本动力学算法[A. F. 沃特尔，《物理评论B》57，R13985（1998）]与希姆和阿马尔的同步子晶格方法[以及，《物理评论B》71，125432（2005）]相结合，对该算法进行了扩展，从而利用事件局部性来提高算法的可扩展性。该算法基于一种域分解，其中样本中不同区域的事件独立发生。我们推导了这种子晶格并行副本动力学算法给出的加速比的解析表达式，并将其与并行MD和传统并行副本动力学进行比较。我们展示了这种算法，尽管引入了对事件局部性的轻微额外近似，但如何能够研究传统方法无法企及的物理系统，并有望更好地利用当前高性能计算机和未来百亿亿次计算机的资源。
