PECANS
PECANS: A parallel environment for cellular automata modeling. This paper is dedicated to a new methodological approach for the modeling of complex physical systems and a parallel programming environment that implements it. N. Margolus and T. Toffoli [ibid. 1, No. 5, 967-993 (1987; Zbl 0655.68055)]. The methodological approach is based on a reduction process of a physical phenomenon in components; each component is represented in terms of a cellular automaton (CA) and the relations among different components are represented by the CA network abstraction. A CA is a bidimensional grid of identical cells; the changes of cell states are represented, at each time step, by the data variation on the grid according to a transition rule; this rule specifies the new state for each cell according to its state and the state of neighbor cells at the previous time step. In addition the CA approach offers the following main advantages. It is strongly expressive since the model description is attained by means of local rules, which also allows the implementation of qualitative aspects of the model. The computational model is intrinsically parallel. The main results of this paper are as follows. In order to apply this methodology a “Parallel environment for cellular automata network simulation” (PECANS) has been developed [M. Mango Furnari, F. Mele and R. Napolitano, in Second Workshop on Massive Parallelism, edited by M. Mango Furnari (Capri, Italy, October 3-7, World Scientific Press (1994)]; this environment allows the parallel execution of physical phenomena simulation by means of a CA network. One of the main objectives was the definition of a new programming language, specifically dedicated to implement the proposed approach. The developed cellular automata network language (CANL) consists mainly of the following. A set of constructs to describe the components of the whole model. A composition mechanism for such components. In PEACANS two main kinds of parallelism are possible. The first one is the data parallelism intrinsic to the CA computational model, because the CA computing rules are essentially local. The second one is the control parallelism form which concerns the network of CAs, while the data parallelism concerns each model component. It is necessary to mention a close relation of the developed method to homogeneous structures, such as the theory of periodically defined transformations on polydimensional abstract registers
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References in zbMATH (referenced in 5 articles , 1 standard article )
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Sorted by year (- Calidonna, C. R.; Naddeo, A.; Trunfio, G. A.; Di Gregorio, S.: From classical infinite space-time CA to a hybrid CA model for natural sciences modeling (2012)
- Guisado, J. L.; Jiménez-Morales, F.; Guerra, J. M.; Fernández De Vega, F.; Iskra, K. A.; Sloot, P. M. A.; Lombraña González, Daniel: Laser dynamics modelling and simulation: an application of dynamic load balancing of parallel cellular automata (2010)
- Calidonna, C. R.; Di Gregorio, S.; Furnari, M. Mango: Mapping applications of cellular automata into applications of cellular automata networks (2002)
- Schoneveld, A.; Sloot, P. M. A.; Lees, M.; Karyadi, E.: A framework for dynamic load balancing: A case study on explosive containment simulation (2000)
- Carotenuto, Luigi; Mele, Frano; Mango Furnari, Mario; Napolitano, Renata: PECANS: A parallel environment for cellular automata modeling (1996)