Incorporating Performance Portability and Data-Oriented Design in Phase-Field Modeling

The advancements in modern computer architecture have made it possible for the simulation of large-scale problems that are of industrial application. However, it can be time-intensive to continually port scientific codes to the ever-evolving computer architectures. Therefore, it is important to develop computational tools that make it easy for researchers to port their existing scientific software or develop new software that can run optimally on current and future computer architectures from different vendors. Also, it often advantageous for scientific software to leverage fine-grained parallelism on homogeneous architectures that are comprised of multi-core CPUs and on heterogeneous computer architectures comprised of multi-core CPUs and GPUs. This paper demonstrates the incorporation of performance portability and data-oriented design paradigms in the phase-field modeling of microstructure evolution. To achieve this, we utilized MATAR, a C+ + software library that allows the straightforward creation and usage of multidimensional and multi-size dense or sparse matrix and array data structures that are also portable across disparate architectures using Kokkos. As a case study, we use the phase-field model for spinodal decomposition which numerically solves the Cahn-Hilliard equation using the semi-implicit Fourier spectral method. Performance portability across multi-core CPUs and different GPU architectures are investigated. Additionally, numerical accuracy and performance gains between single and double precision phase-field calculations are investigated.