Davide Spataro

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GPGPU (General Purpose computing on Graphics Processing Units) has marked a revolution in the field of Parallel Computing allowing to achieve computational performance unimaginable until a few years ago. This hardware has proven to be extremely reliable and suitable to simulate Cellular Automata (CA) models for modeling complex systems whose evolution can(More)
Cellular Automata represent a formal frame for dynamical systems which evolve on the base of local interactions. We here present first results of the CUDA parallelization of the SCIDDICA S3-hex Complex Cellular Automata model for simulating debris flows. In particular, a first strategy for the parallelization of the model is based on a straightforward one(More)
This paper presents the release fv3 of the Complex Cellular Automata model Sciara for simulating lava flows. It 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 model has been preliminary tested with satisfying results by considering the 2006 lava event at Mt(More)
Birds flocking is an interesting natural phenomenon to study as proven by numerous papers in this field. In this paper, we present a GPGPU model for birds flocking simulation using NVIDIA's CUDA framework. This technology has been widely adopted in computational science and have dramatically increased computation performances. Using the autonomous agent(More)
The performance and scalability of cellular automata, when executed on parallel/distributed machines, are limited by the necessity of synchronizing all the nodes at each time step, i.e., a node can execute its code only after all the other nodes have executed the previous step. However, if the code is parallelized by partitioning the space of the automata,(More)
Particle tracking plays an important role in numerous fields of science. In this paper, we present TraCCA, an algorithm for detecting and tracking particles based on geometrical difference evaluation and centroid displacement analysis to reconstruct the trajectories. This method works for n-dimensional input data provided that particles are represented by(More)
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