Abstract
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.
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Notes
- 1.
Although sophisticated GPU-based methods specialized for viscoelastic crustal deformation analysis and specific GPU architecture is proposed [19], we compare with generally available solvers stated above for readability.
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Acknowledgements
Computational resource of AI Bridging Cloud Infrastructure (ABCI) provided by National Institute of Advanced Industrial Science and Technology (AIST) was used. This work was supported by MEXT as “Program for Promoting Researches on the Supercomputer Fugaku” (Large-scale numerical simulation of earthquake generation, wave propagation and soil amplification, JPMXP1020200203). This work was supported by JSPS KAKENHI Grant Numbers 18H05239, 22K12057, 22K18823. This work was supported by MEXT, under its Earthquake and Volcano Hazards Observation and Research Program. This work was supported by JST SPRING, Grant Number JPMJSP2108.
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Murakami, S. et al. (2023). Development of 3D Viscoelastic Crustal Deformation Analysis Solver with Data-Driven Method on GPU. In: Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2023. ICCS 2023. Lecture Notes in Computer Science, vol 14074. Springer, Cham. https://doi.org/10.1007/978-3-031-36021-3_45
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