3:30 PM - 3:45 PM
[STT51-01] Acceleration of crustal deformation computation using GPUs and its application to stochastic inversion analysis with geometry uncertainty
Keywords:Finite Element Analysis, OpenACC, Conjugate Gradient method, Element-by-Element method
Multiple crustal deformation computations enable stochastic inverse analysis, optimization, sensitivity analysis, and Monte Carlo simulation. These applications are important in considering uncertainties, including those in material properties, geometries, and inputs. However, the computation cost of such simulations increases depending on the number of repetitive computations required.
GPUs have recently become common in scientific computing. It is thought that they are broadly applicable to numerical simulations with parallel computation. Use of GPU accelerators is expected to speed up simulations. However, GPU calculations often become memory bandwidth bound computation; thus it is difficult to exhibit high performance in a straightforward implementation. Here, we propose a method for computing elastic crustal deformation using a fast solver with multiple GPUs. We modified the algorithm according to the hardware architecture in the GPU. As for the sparse matrix-vector product, which accounts for the largest proportion of the computation time, we introduced the Element-by-Element method and reduced the amount of memory access.
To test the proposed method, we estimated the coseismic slip distribution by multiple crustal deformation computations. We targeted the northeastern Japan and generated FE models which have about 80,000,000 DOF. We computed 360,000 forward analyses (360 forward analyses x 1,000 different FE models) and conducted a stochastic inverse analysis. These elastic crustal deformation computations were computed in nine days by using a GPU cluster comprising 16 CPUs (Intel Xeon E5-2695 v2) and 64 GPUs (NVIDIA K40). We calculated the average value and standard deviation of coseismic slip distribution for 1,000 cases. The standard deviation of the slip distribution was 13% of the average value. This indicates that consideration of uncertainties in geometry is significant because the obtained standard deviation is non-negligible when discussing the coseismic slip distribution and related stress change distribution. Using our proposed method, a stochastic estimation of coseismic slip distribution, with uncertainties in geometry, was computed within a realistic timeframe. In future studies, we will apply this method to the optimization of crustal structure.