GPU-accelerated Multiphysics-based Seismic Wave Propagation
Simulation and its Surrogate Model with Machine Learning

1

Ryota Kusakabe

Tsuyoshi Ichimura

Kohei Fujita

The University of TokyoJapan
ryota-k@eri.u-tokyo.ac.jp

The University of TokyoJapan
ichimura@eri.u-tokyo.ac.jp

The University of TokyoJapan
fujita@eri.u-tokyo.ac.jp

Muneo Hori

Lalith Wijerathne

Japan Agency for Marine-Earth
Science and TechnologyJapanhorimune@jamstec.go.jp

The University of TokyoJapan
lalith@eri.u-tokyo.ac.jp

INTRODUCTION

Physics-based earthquake simulation is expected to contribute toestimation and mitigation of earthquake damage. However, its computational cost is huge, especially for three-dimensional (3D) domain. To overcome the cost, we have been developing a variety ofmethods for large-scale 3D earthquake simulation using the finiteelement method (FEM). In this poster, the GPU-accelerated method
for multiphysics seismic wave propagation simulation considering soil liquefaction [1] is presented. By adopting load balancing
schemes that consider characteristics of the multiphysics problem, the developed method achieved a 10-fold speedup over theCPU-based method. In addition, with machine learning using thesimulation results, a surrogate model was constructed to enablemuch faster evaluation of earthquake response.

2GPU-ACCELERATED EARTHQUAKESIMULATION

The governing equation is the motion equation of the soil:
𝜕 2 𝒖 𝜕𝝈−= 𝒇,
(1)
𝜕𝒙
𝜕𝑡 2
where 𝝈 is the stress, 𝒖 is the displacement of the soil, 𝜌 is the density of the soil, 𝒇 is the external force, and 𝑡 is the time. The equation(1) is discretized by FEM and Newmark 𝛽 method, and a sparse matrix equation with 1 million – 1 billion degrees of freedom (DOF) isobtained. The equation, whose coefficient matrix is updated every
time step, is solved over tens of thousands of time steps. The developed method uses the adaptive conjugate gradient method to solveit. Here, a multigrid method and mixed precision arithmetic areused in the preconditioner to reduce the computational cost. The
whole analysis is parallelized with MPI and OpenACC. By revising
the domain partitioning method and adopting element reorderingthat consider the characteristics of the multiphysics problem, wedeveloped an algorithm with high parallel efficiency which is suitable for GPU architecture. Also, a 21-bit floating point data type,
FP21, is used in the MPI communication in the preconditioner toreduce communication cost. Even though the target multiphysics
simulation is complex and could be unstable, the developed method
can stably perform it.
In the performance measurement on one compute node of thesupercomputer AI Bridging Cloud Infrastructure (ABCI) [2] with𝜌

four Nvidia V100 GPUs, the developed method was 10 times fasterthan the method that used only CPUs. The developed method carried out a 30,000-time-step simulation with an 89,146,716-DOF soil
structure model using 13 compute nodes on ABCI for 3 h 33 min.
When this simulation was performed on a CPU-based supercomputer with Intel Xeon Phi 7250 (Knights Landing) CPUs, it took 14h 37 min with 128 compute nodes, indicating the developed method
drastically reduces necessary computational resources.
More simulation results can be obtained as a result of the simulation speedup. With machine learning using the obtained results, weconstructed a neural network surrogate model of earthquake simulation and enabled even faster evaluation of earthquake response.

3CONCLUSIONWe accelerated large-scale earthquake simulation by using GPU.
The implementation for one constitutive law was presented in thisposter as an example, but the developed method is versatile andis applicable to other constitutive laws. Also, the surrogate model
constructed from simulation results enabled even faster evaluationof complex multiphysics earthquake response. It is expected toenable evaluation of earthquake response of large-scale domain fora large number of cases, which is not feasible with physics-based
simulation due to the huge computational cost.

ACKNOWLEDGMENTSComputational resource of AI Bridging Cloud Infrastructure (ABCI)
provided by National Institute of Advanced Industrial Science andTechnology (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: hp200126, hp210171) and JSPS
KAKENHI Grant Numbers JP20J22348, JP18H05239, JP18H03795,
JP17K14719.

REFERENCES[1] Ryota Kusakabe, Kohei Fujita, Tsuyoshi Ichimura, Takuma Yamaguchi, Muneo
Hori, and Lalith Wijerathne. 2021. Development of regional simulation of seismic
ground-motion and induced liquefaction enhanced by GPU computing. Earthquake Engineering and Structural Dynamics 50 (2021), 197–213.
[2] National Institute of Advanced Industrial Science and Technology. 2021. AboutABCI. Retrieved November 26, 2021 from https://abci.ai/en/about_abci/