Acceleration of Cellular Automata through Parallel Computing with OpenCL

Maelso Bruno Pacheco Nunes Pereira, Christian Azambuja Pagot, Josue da Silva Gomes Junior, Jorge Gabriel Gomes de Souza Ramos, Tiago P. Nascimento, Alisson V. Brito
Centro de Informatica – CI, Universidade Federal da Paraiba – UFPB, Joao Pessoa, Brazil
8th Workshop on Applications for Multi-Core Architectures (WAMCA), 2017


   title={Acceleration of Cellular Automata through Parallel Computing with OpenCL},

   author={Pereira, Maelso Bruno Pacheco Nunes and Pagot, Christian Azambuja and Junior, Josue da Silva Gomes and de Souza Ramos, Jorge Gabriel Gomes and Nascimento, Tiago P and Brito, Alisson V},

   booktitle={2017 International Symposium on Computer Architecture and High Performance Computing Workshops (SBAC-PADW)},





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Cellular Automata (CA) have its origins in the work of Von Neumann and, since then, have become an important research topic with a wide range of applications, ranging from DNA sequencing to ecological dynamics. One aspect that may be of interest during a CA simulation is the evolution in the number of individuals of each species along time. This analysis can give important information about the dominance of certain species in a dynamical system, or identify aspects that might favor one or more species in detriment of others. CA simulations can be computationally expensive tasks. Depending on the simulation domain size, number of dimensions or the number of individuals, these simulations can take several hours to complete. The evaluation of the number of individuals at each simulation step is an equally expensive task. Several acceleration techniques have been developed to improve the performance of CA simulations, and some of them take into account the evolution in the number of individuals along the simulation. In this work we propose an efficient CA simulator which is capable of efficiently evaluate the evolution in the number of individuals of each species. High performance is obtained through the use of the massive parallelism of GPUs. The presented approach achieved a speed-up of 44 times when compared to a sequential implementation, and 26 times when compared to a traditional approach also in GPU.
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