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This paper presents the parallel implementation, using the Compute Unified Device Architecture (CUDA) architecture, of the SCIARA-fv3 Complex Cellular Automata model for simulating lava flows. The computational model is based on a Bingham-like rheology and both flow velocity and the physical time corresponding to a computational step have been made explicit. The parallelization design has involved, among other issues, the application of strategies that can avoid incorrect computation results due to race conditions and achieving the best performance and occupancy of the underlying available hardware. Two hardware types were adopted for testing different versions of the CUDA implementations of the SCIARA-fv3 model, namely the GTX 580 and GTX 680 graphic processors. Despite its computational complexity, carried out experiments of the model parallelization have shown significant performance improvements, confirming that graphic hardware can represent a valid solution for the implementation of Cellular Automata models. [ABSTRACT FROM AUTHOR]

Simulation of the nonlinear mechanical response of materials with explicit representation of microstructural features is extremely challenging. These models typically involve a very large number of degrees of freedom, and are prone to convergence difficulties when searching for roots to nonlinear equilibrium equations. We focus on an idealized material model that is motivated by the microstructure of synthetic nacre: individual ‘bricks’ (representing ceramic platelets) interact through nonlinear cohesive springs (representing a small volume fraction of polymer that bonds the platelets). The model simulates composite fracture through rupture of the cohesive springs. The problem is cast in terms of energy minimization and is essentially described by ‘nearest neighbor’ interactions. The principal focus of this paper is to illustrate the computational gains achievable by the strategic marriage of robust, global Monte Carlo minimization algorithms to the graphics processing unit architecture, and to describe how they were realized on the Nvidia GPU. Results comparing the computation times for graphics processing unit and central processing unit implementations demonstrate that a new adaptive version of the simulated annealing algorithm yields a speedup of approximately 5 times, whereas the graphics processing unit implementation yields a speed-up of about 16 times over conventional four-core central processing unit implementations. The resulting speed enhancement for adaptive graphics processing unit minimization of a factor of 80 enables a far broader range of simulations than has previously been possible. Simulations involving as many as 300,000 bricks can be performed in hours, as compared to weeks required by central processing unit implementation. Many aspects of this approach are translatable to other physical problems involving energy minimization in systems with large numbers of degrees of freedom. [ABSTRACT FROM AUTHOR]

A combination of reaction–diffusion models with moving-boundary problems yields a system in which the diffusion (spreading and penetration) and reaction (transformation) evolve the system's state and geometry over time. These systems can be used in a wide range of engineering applications. In this study, as an example of such a system, the degradation of metallic materials is investigated. A mathematical model is constructed of the diffusion-reaction processes and the movement of corrosion front of a magnesium block floating in a chemical solution. The corresponding parallelized computational model is implemented using the finite element method, and the weak and strong-scaling behaviors of the model are evaluated to analyze the performance and efficiency of the employed high-performance computing techniques. [ABSTRACT FROM AUTHOR]

We report the realization of the first cosmological simulations on the scale of the whole observable universe. These simulations have been carried out on 4752 nodes of the Curie supercomputer as a part of the Dark Energy Universe Simulation: Full Universe Runs (DEUS-FUR) project which aims at establishing new probes to put constraints on the nature of dark energy by comparing the growth of large-scale structures, the characteristics of extreme statistical events and the matter distribution in redshift space. The numerical challenge of the first DEUS-FUR simulation associated with the concordance ΛCDM (Λ Cold Dark Matter) model was already presented during the 2012 supercomputing conference (Alimi et al., 2012, in The international conference for high performance computing, networking, storage and analysis). Here we first focus on the numerical aspects of the two new simulations. In practice, each one of these simulations has evolved 550 billion dark matter particles in an adaptive mesh refinement grid, and one of the new simulations has pushed back the total number of grid points from 2000 billion for the ΛCDM model to 2200 billion due to the formation of a larger number of structures. We highlight the optimizations and adjustments required to run such a set of simulations and we then summarize some important lessons learnt for future exascale computing projects. [ABSTRACT FROM AUTHOR]