Development of 3D Viscoelastic Crustal Deformation Analysis Solver with Data-Driven Method on GPU

In this paper, we developed a 3D viscoelastic analysis solver with a data-driven method on GPUs for fast computation of highly detailed 3D crustal structure models. Here, the initial solution is obtained with high accuracy using a data-driven predictor based on previous time-step results, which reduces the number of multi-grid solver iterations and thus reduces the computation cost. To realize memory saving and high performance on GPUs, the previous time step results are compressed by multiplying a random matrix, and multiple Green’s functions are solved simultaneously to improve the memory-bound matrix-vector product kernel. The developed GPU-based solver attained an 8.6-fold speedup from the state-of-art multi-grid solver when measured on compute nodes of AI Bridging Cloud Infrastructure at National Institute of Advanced Industrial Science and Technology. The fast analysis method enabled calculating 372 viscoelastic Green’s functions for a large-scale 3D crustal model of the Nankai Trough region with $$4.2\times 10^9$$ degrees of freedom within 333 s per time step using 160 A100 GPUs, and such results were used to estimate coseismic slip distribution.