Abstract: Crustal deformation computation using 3-D high-fidelity models has been in heavy demand due to accumulation of observational data. This approach is computationally expensive and more than 100,000 repetitive computations are required for various application including Monte Carlo simulation, stochastic inverse analysis, and optimization. To handle the massive computation cost, we develop a fast Finite Element (FE) analysis method using multi-GPUs for crustal deformation. We use algorithms appropriate for GPUs and accelerate calculations such as sparse matrix-vector product. By reducing the computation time, we are able to conduct multiple crustal deformation computations in a feasible timeframe. As an application example, we conduct stochastic inverse analysis considering uncertainties in geometry and estimate coseismic slip distribution in the 2011 Tohoku Earthquake, by performing 360,000 crustal deformation computations for different 80,000,000 DOF FE models using the proposed method.

Abstract: Earth System Models are critical tools for the study of our climate and its future trends. These models are in constant evolution and their growing complexity entails an incrementing demand of the resources they require. Since the cost of using these state-of-the-art models is huge, looking closely at the factors that are able to impact their computational performance is mandatory. In the case of the state-of-the-art ocean model NEMO (Nucleus for European Modelling of the Ocean), used in many projects around the world, not enough attention has been given to the domain decomposition. In this work we show the impact that the selection of a particular domain decomposition can have on computational performance and how the proposed methodology substantially improves it.

Abstract: This paper presents data management and strategies for implementing smoothed particle hydrodynamics (SPH) method to simulate volcano plumes. These simulations require a careful definition of the domain of interest and multi-phase material involved in the flow, both of which change over time and involve transport over vast distances in a short time. Computational strategies are developed to overcome these challenges by building mechanisms for efficient creation and deletion of particles for simulation, parallel processing (using the message passing interface (MPI)) and a dynamically defined halo domain (a domain that "optimally" captures all the material involved in the flow). A background grid is adopted to reduce neighbor search costs and to decompose the domain. A Space Filing Curve (SFC) based ordering is used to assign unique identifiers to background grid entities and particles. Time-dependent SFC based indices are assigned to particles to guarantee uniqueness of the identifier. Both particles and background grids are managed by hash tables which can ensure quick and flexible access. An SFC based three dimensional (3D) domain decomposition and a dynamic load balancing strategy are implemented to ensure good load balance. Several strategies are developed to improve performance: dynamic halo domains, calibrated particle weight and optimized work load check intervals. Numerical tests show that our code has good scalability and performance. The strategies described in this paper can be further applied to many other implementations of mesh-free methods, especially those implementations that require flexibility in adding and deleting of particles.