Multi-architecture Monte-Carlo (MC) simulation of soft coarse-grained polymeric materials: SOft coarse grained Monte-Carlo Acceleration (SOMA)

Multi-component polymer systems are important for the development of new materials because of their ability to phase-separate or self-assemble into nano-structures. The Single-Chain-in-Mean-Field (SCMF) algorithm in conjunction with a soft, coarse-grained polymer model is an established technique to investigate these soft-matter systems. Here we present an implementation of this method: SOft coarse grained Monte-carlo Acceleration (SOMA). It is suitable to simulate large system sizes with up to billions of particles, yet versatile enough to study properties of different kinds of molecular architectures and interactions.

We achieve efficiency of the simulations commissioning accelerators like GPUs on both workstations as well as supercomputers. The implementation remains flexible and maintainable because of the implementation of the scientific programming language enhanced by OpenACC pragmas for the accelerators.

We present implementation details and features of the program package, investigate the scalability of our implementation SOMA, and discuss two applications, which cover system sizes that are difficult to reach with other, common particle-based simulation methods.

多组分聚合物系统因具备相分离或自组装成纳米结构的能力，对于新型材料的发展具有重要意义。单链场平均法（Single-Chain-in-Mean-Field，简称SCMF）结合软性粗粒度聚合物模型，是一种研究这些软物质系统的成熟技术。本文档将介绍该方法的实现：SOft coarse grained Monte-carlo Acceleration（SOMA）。该方法适用于模拟高达数十亿粒子的庞大系统，同时具备足够的灵活性，以研究不同类型分子结构及其相互作用特性。通过在工作站及超级计算机上部署GPU等加速器，我们实现了模拟效率的提升。得益于科学编程语言的运用，并辅以OpenACC指令的增强，该实现保持了灵活性和可维护性。本文档将阐述程序包的实施细节和功能特点，探讨SOMA实现的可扩展性，并讨论两个应用案例，这些案例涵盖了其他常见粒子模拟方法难以触及的系统规模。