Multi-Architecture Monte-Carlo (MC) Simulation of Soft Coarse-Grained Polymeric Materials: SOft coarse grained Monte-carlo Acceleration (SOMA)

TL;DR

SOMA is a GPU-accelerated, scalable Monte Carlo simulation tool for large-scale, soft coarse-grained polymer systems, enabling detailed study of complex molecular architectures and interactions.

Contribution

The paper introduces SOMA, a flexible, efficient implementation of the SCMF algorithm optimized with OpenACC for large, multi-component polymer simulations on accelerators.

Findings

SOMA can simulate systems with up to billions of particles.

The implementation scales well on GPUs and supercomputers.

Applications demonstrate the ability to study large, complex polymer systems.

Abstract

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 im- plementation of this method: SOft coarse grained Monte-carlo Accelera- tion (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 implementa- tion 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.