High performance scientific computing by a parallel cellular environment
Future Generation Computer Systems - Special issue on HPCN96
Future Generation Computer Systems - Special issue on cellular automata: promise in computational science
MPI: The Complete Reference
GPU Cluster for High Performance Computing
Proceedings of the 2004 ACM/IEEE conference on Supercomputing
Parallel evolutionary modelling of geological processes
Parallel Computing
A macroscopic collisional model for debris-flows simulation
Environmental Modelling & Software
Using OpenMP: Portable Shared Memory Parallel Programming (Scientific and Engineering Computation)
Using OpenMP: Portable Shared Memory Parallel Programming (Scientific and Engineering Computation)
TeraFLOP computing on a desktop PC with GPUs for 3D CFD
International Journal of Computational Fluid Dynamics - Mesoscopic Methods And Their Applications To CFD
Fundamenta Informaticae - Membrane Computing
Environmental Modelling & Software
Towards a hybrid parallelization of lattice Boltzmann methods
Computers & Mathematics with Applications
Computing and Visualization in Science
LBM based flow simulation using GPU computing processor
Computers & Mathematics with Applications
Proceedings of the 9th international conference on Cellular automata for research and industry
ACRI'10 Proceedings of the 9th international conference on Cellular automata for research and industry
Accelerating wildfire susceptibility mapping through GPGPU
Journal of Parallel and Distributed Computing
Efficient application of GPGPU for lava flow hazard mapping
The Journal of Supercomputing
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This paper presents an efficient implementation of the SCIARA Cellular Automata computational model for simulating lava flows using the Compute Unified Device Architecture CUDA interface developed by NVIDIA and carried out on Graphical Processing Units GPU. GPUs are specifically designated for efficiently processing graphic data sets. However, they are also recently being exploited for achieving excellent computational results for applications non-directly connected with Computer Graphics. The authors show an implementation of SCIARA and present results referred to a Tesla GPU computing processor, a NVIDIA device specifically designed for High Performance Computing, and a Geforce GT 330M commodity graphic card. Their carried out experiments show that significant performance improvements are achieved, over a factor of 100, depending on the problem size and type of performed memory optimization. Experiments have confirmed the effectiveness and validity of adopting graphics hardware as an alternative to expensive hardware solutions, such as cluster or multi-core machines, for the implementation of Cellular Automata models.