Parallel Implementation of Totalistic Cellular Automata Model of Stable Patterns Formation

Kireeva, A. E.

doi:10.1007/978-3-642-39958-9_31

Cited by 6 publications

(4 citation statements)

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“…An intense desire to reduce the simulation time yields in choosing a minimal m = |T Σ |. On the other hand, Monte Carlo methods [3], as well as the asynchronous CA models [7], are based on the principle of maximal stochasticity. There is no theoretical answer to the question in what extent it is admissible to vary stochasticity without violating a given simulation accuracy.…”

Section: The Simulation Results a Reaction-diffusion Process Identifmentioning

confidence: 99%

“…Among the existing computer simulation methodologies of reaction-diffusion phenomena completely or partly based on the above statements, the following methods are most known: kinetic Monte Carlo methods in chemistry [2,3] and in microelectronics [4], probabilistic cellular automata in material science [5][6][7], self-organizing CA in biology [8,9]. Actually, all of them may be considered as asynchronous cellular automata [10] simulating phenomena which are composed of movements and transformations [11] of a certain kind of micro-entities, such as molecules or nano particles either real or abstract.…”

Section: Introductionmentioning

confidence: 99%

“…which provides that any pair of cells (x i , x j ) belonging to one and the same Π is separated by the distance d ij > R(T ), hence, can be updated simultaneously, i.e. synchronously, without violating (7). Investigation of computational properties of block-synchronous CA [15,16] has shown that block-synchronous CA are significantly less time consuming than asynchronous CA, enhancing their use in sequential cases as well.…”

mentioning

confidence: 99%

“…So, l ρ,µ causes a decrease in efficiency, being dependent on the global mode of operation Φ ρ,µ . According to (7), a simple synchronous CA ℵ σ requires inter-processor data exchange only once per iteration, i.e. l σ = 1.…”

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confidence: 99%

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Simulation Performance Versus Stochasticity in Large-Scale Cellular Automata Models

Bandman¹

2016

bncc.cs

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Due to a growing interest in chemical and biological phenomena, simulation of reaction-diffusion processes on micro-level becomes urgently wanted. Asynchronous cellular automata are promising mathematical models to be used as a base for creating computer simulation systems, which gives reason for the investigation of their capability. In particular, since the micro-level simulation requires a very large size of cellular automata, the performance of simulation is important, especially, because parallel implementation is inevitable. In this connection, the dependence of simulation performance on the CA stochasticity (the degree of randomness) is studied in this paper. Much attention is being given to the contradiction between the stochasticity and the parallelization efficiency. It is shown how the proper choice of the CA stochasticity may help to achieve the acceptable performance. The results of a 3D process of diffusion limited aggregation are presented to assert the dependencies obtained.

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Section: The Simulation Results a Reaction-diffusion Process Identifmentioning

confidence: 99%