Accelerating phase-field simulations on exascale computing systems for faster-than-real-time precipitate aging predictions
Year: 2026
Type: preprint
Abstract: Three-dimensional phase-field simulations are a gold-standard for microstructure prediction for materials, but their computational cost often limits application-relevant calculations to modest domain sizes and timescales. Here, we present a holistic approach for accelerating Fourier pseudospectral phase-field simulations by combining performance-portable GPU computing, large-scale distributed-memory parallelism, and high-order implicit-explicit time integration within the MEUMAPPS C++ framework. We demonstrate this approach with a 1.5-billion-grid-point simulation of the growth and coarsening of 1,920 gamma'' precipitates in a Ni-Nb-Fe alloy. The simulated seven-hour heat treatment is completed in 5.5 hours, making the calculation faster than real time and an estimated 217-501x faster than a CPU-only, first-order baseline. This performance makes three-dimensional simulations of thousands of interacting precipitates tractable, enabling quantitative studies ocollective microstructural phenomena, large simulation ensembles, and real-time integration into controlling and interpreting experiments. Benchmarking shows single-node GPU speedups of up to 17.5x relative to comparable CPU resources, near-ideal strong scaling to 4096 GPUs for a 12-billion-grid-point problem, and a further 2.6-2.9x acceleration from fourth-order time integration at scientifically relevant error tolerances. These results establish a performance-portable strategy for exploiting leadership-scale GPU systems that is applicable to a broad class of Fourier pseudospectral simulations beyond phase-field models such as fluid dynamics and crystal plasticity simulations.
Source: arXiv (Cornell University)
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G1026679911, G121915436, G1356433355, G14554683, G1645119126, G1796307089, G203625172, G2082431150, G2145270357, G217780377, G2366088775, G313676650, G3669113783, G3886122786, G3997560910, G4017386982, G4094752214, G4370606643, G4423657506, G5685548091, G5692331544, G574795485, G6086734019, G675320460, G816254390, G8269158468, G8943143067, G895790057, G991281326
OpenAlex ID: W7214993748