Damage to buried pipelines caused by local amplification of seismic ground motion in highly nonuniform grounds is not yet fully understood. The development of methods to evaluate the amplification of ground motion in complex ground structures is thus desirable. Here, we report large-scale nonlinear seismic ground response analysis using a 3D nonlinear finite element method (FEM) and attempt to reproduce observed seismic ground motion. We also discuss the strain amplification processes and their effects on buried pipelines in detail. The findings are expected to aid in improving the seismic resistance of buried pipelines.

A low-order implicit unstructured finite-element solver for Earth's crust deformation problem is proposed. The solver scales up to 294,912 CPU cores of K computer with 94.8% size-up efficiency, enabling solving a 504 billion degrees-of-freedom crust deformation problem with 126 billion second-order tetrahedral elements in 146.5 s at 0.970 PFLOPS (20.6% of peak) on 294,912 CPU cores. This is 11.1 times faster than a highly-tuned preconditioned conjugate gradient solver and 2.18 times faster than the state-of-the-art SC14 Gordon Bell Prize Finalist solver. With the proposed solver, we solved a practical 381 billion degrees-of-freedom Eastern Japan crust deformation problem (576 km x 800 km x 400 km area; resolution of 62.5 m) in 252.2 s using 294,912 CPU cores. Such high-resolution analysis assures the convergence of stress and strain required to evaluate nonlinear material constitutive models at plate boundaries and is expected to become a key tool for physics-based earthquake forecasting.

We present a scalable high-performance computing implementation of an agent-based economic model using distributed + shared-memory hybrid parallelization paradigms, capable of simulating 1:1 scale models of large economies like the eurozone. Agent-based economic models consist of millions of agents interacting over several graphs, which are either centralized or scale-free in nature. While most of the interactions are bi-directional, the interaction graphs are dense and random and keep evolving as the simulation progresses. These characteristics cause a very large and unknown number of random communications among MPI processes, posing challenges to developing scalable parallel extensions. Further, random access to large volume of data makes the algorithms highly memory-bound, severely degrading computational performance. Adopting various strategies inspired by the real-world functioning of economies, we reduce the large unknown number of communications to a known handful number. Memory-intensive algorithms are improved to make these cache-efficient, and advanced MPI functions are used to minimize communication overhead, thereby attaining higher performance and scalability. Further, an MPI + OpenMP hybrid model is developed to best utilize modern many-core computing nodes with low per-core memory capacity. It is demonstrated that our implementation can simulate a full fledged economic model with 331 million agents within 108 seconds using 128 CPU cores attaining 70 percent strong scalability.