# A highly efficient 3D level-set grain growth algorithm tailored for ccNUMA architecture

## TL;DR

This paper presents a highly efficient 3D level-set grain growth algorithm optimized for ccNUMA architectures, enabling scalable simulations of microstructure evolution in polycrystals and magnetic materials.

## Contribution

The paper introduces a novel parallel level-set method tailored for ccNUMA systems, improving performance and scalability in 3D grain growth simulations.

## Key findings

- Achieved strong scalability on ccNUMA architectures.
- Successfully simulated ideal and non-ideal grain growth in 3D.
- Modeled microstructure evolution in magnetic materials under external fields.

## Abstract

A highly efficient simulation model for 2D and 3D grain growth and recrystallization was developed based on the level-set method. The model introduces modern computational concepts to achieve excellent performance on parallel computer architectures. Strong scalability was measured on ccNUMA architectures. To achieve this, the proposed approach considers the application of local level-set functions at the grain level. Ideal and non-ideal grain growth was simulated in 3D with the objective to study the evolution of statistical representative volume elements in polycrystals. In addition, microstructure evolution in an anisotropic magnetic material affected by an external magnetic field was simulated.