A P System Based Model of an Ecosystem of Some Scavenger Birds

Cardona, Mónica; Colomer, M. Angels; Margalida, Antoni; Pérez-Hurtado, Ignacio; Pérez-Jiménez, Mario J.; Sanuy, Delfi

doi:10.1007/978-3-642-11467-0_14

Cited by 42 publications

(46 citation statements)

References 5 publications

(15 reference statements)

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“…(A delicate issue can appear in the case of components which can send objects to the environment and bring objects from the environment -this happens, for instance, for symport/antiport P systems; in this case we have to decide whether or not the components can exchange objects by means of the environment, or the only permitted communication is done by means of the rules in R. For instance, a "local environment" for each component can be considered, disjoint from the "local environments" of other components, thus preventing the interaction of components by means of other rules than those in R. Actually, the rules in R themselves can be defined between these "local environments" -which is a variant worth to explore. We point out here that also the need of a "local environment" has appeared in the applications of membrane computing to eco-systems investigations, see [4] and its references. )…”

Section: Dp Systems -A Preliminary Formalizationmentioning

confidence: 91%

Solving Problems in a DistributedWay in Membrane Computing: dP Systems

Paun¹,

Pérez-Jiménez²

2010

INT J COMPUT COMMUN

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Although P systems are distributed parallel computing devices, no explicit way of handling the input in a distributed way in this framework was considered so far. This note proposes a distributed architecture (based on cell-like P systems, with their skin membranes communicating through channels as in tissue-like P systems, according to specified rules of the antiport type), where parts of a problem can be introduced as inputs in various components and then processed in parallel. The respective devices are called dP systems, with the case of accepting strings called dP automata. The communication complexity can be evaluated in various ways: statically (counting the communication rules in a dP system which solves a given problem), or dynamically (counting the number of communication steps, of communication rules used in a computation, or the number of objects communicated). For each measure, two notions of "parallelizability" can be introduced. Besides (informal) definitions, some illustrations of these idea are provided for dP automata: each regular language is "weakly parallelizable" (i.e., it can be recognized in this framework, using a constant number of communication steps), and there are languages of various types with respect to Chomsky hierarchy which are "efficiently parallelizable" (they are parallelizable and, moreover, are accepted in a faster way by a dP automaton than by a single P automaton). Several suggestions for further research are made.

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Section: Dp Systems -A Preliminary Formalizationmentioning

confidence: 91%

Solving Problems in a DistributedWay in Membrane Computing: dP Systems

Paun¹,

Pérez-Jiménez²

2010

INT J COMPUT COMMUN

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“…In order to exploit those luring features, a P systems based general framework for modeling ecosystems dynamics was presented in Cardona et al (2010a). This tool has been used to model real ecosystems computationally, being two good exponents the scavenger birds of the Catalan Pyrenees (Spain) (Cardona et al 2010b) and the zebra mussel in a reservoir at Ribarroja (Spain) (Cardona et al 2010a). The ultimate goal is to assist ecologists to adopt a priori management strategies for the real system by executing virtual experiments on a simulator developed ad hoc for these P systems based models (Martínez-del-Amor et al 2010).…”

Section: Future Workmentioning

confidence: 99%

The GPU on the simulation of cellular computing models

et al. 2011

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Membrane Computing is a discipline aiming to abstract formal computing models, called membrane systems or P systems, from the structure and functioning of the living cells as well as from the cooperation of cells in tissues, organs, and other higher order structures. This framework provides polynomial time solutions to NP-complete problems by trading space for time, and whose efficient simulation poses challenges in three different aspects: an intrinsic massively parallelism of P systems, an exponential computational workspace, and a non-intensive floating point nature.In this paper, we analyze the simulation of a family of recognizer P systems with active membranes that solves the Satisfiability problem in linear time on different instances of Graphics Processing Units (GPUs). For an efficient handling of the exponential workspace created by the P systems computation, we enable different data policies to increase memory bandwidth and exploit data locality through tiling and dynamic queues. Parallelism inherent to the target P system is also managed to demonstrate that GPUs offer a valid alternative for high-performance computing at a considerably lower cost. Furthermore, scalability is demonstrated on the way to the largest problem size we were able to run, and considering the new hardware generation from Nvidia, Fermi, for a total speed-up exceeding four orders of magnitude when running our simulations on the Tesla S2050 server.

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“…With the aim of improving the model, the authors are adding ingredients to it, as new species and more complex behaviour for the animals. The improved model, together with results of virtual experiments made with this software application, is exhaustively described in [2].…”

Section: A Tool For Simulating Ecosystems Based On P-linguamentioning

confidence: 99%

P–Lingua 2.0: A software framework for cell–like P systems

García-Quismondo¹,

Gutiérrez-Escudero

Martínez-del-Amor

et al. 2009

INT J COMPUT COMMUN

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P-Lingua is a programming language for membrane computing. It was first presented in Edinburgh, during the Ninth Workshop on Membrane Computing (WMC9). In this paper, the models, simulators and formats included in P-Lingua in version 2.0 are explained. We focus on the stochastic model, associated simulators and updated features. Finally, we present one of the first applications based on PLingua: a tool for describing and simulating ecosystems.

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A P System Based Model of an Ecosystem of Some Scavenger Birds

Abstract: Since nature is very complex, the perfect model that explains it will be complex too. A complex model is not practical or good to use, so we should obtain a simple and useful model that keeps the most important natural factors.

Cited by 42 publications

References 5 publications

Solving Problems in a DistributedWay in Membrane Computing: dP Systems

Solving Problems in a DistributedWay in Membrane Computing: dP Systems

The GPU on the simulation of cellular computing models

P–Lingua 2.0: A software framework for cell–like P systems

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