An Improved GPU Simulator for Spiking Neural P Systems

Cabarle, Francis George C.; Adorna, Henry N.; Amor, Miguel Ángel Martínez del

doi:10.1109/bic-ta.2011.37

Cited by 14 publications

(16 citation statements)

References 10 publications

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“…Among other works detailed there, we can recall the line followed along PMCGPU project [163]. Please look for the details about parallel simulators in [9], including among others [118][119][120][121][122][123][124][125][126][127][128]. Some more recent results appear after that surveys, including [10,129].…”

Section: The Era Of Practical Applications Based On P Systemsmentioning

confidence: 99%

An interactive timeline of simulators in membrane computing

et al. 2019

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As with any fast-emerging research front in computer science, the proliferation of theoretical and practical results within Membrane computing since its appearance in 1998 was astonishing. As a consequence, it became necessary during the subsequent years to produce several surveys collecting the main achievements from a theoretical point of view, along with some specific surveys about simulation tools for this paradigm. As the discipline has reached a certain degree of maturity, more practical applications have arisen, and new collective works are summarising the new software products appeared. However, while these recapitulation efforts remain useful for details about new simulators, they cannot act as exhaustive updated listings, as they become obsolete as soon as new tools are developed. Thus, we considered that it was necessary to provide an interactive tool showing an updated timeline (https ://www.gcn.us.es/Simul ation MC) about the simulation of the computational devices of membrane computing (a.k.a P systems), aiming to stay updated whenever any new practical work comes out in the discipline. This paper recalls the main stages and milestones within the evolution of simulation tools for different types and variants of P systems, along with their main related applications. In addition, it describes the interactive web tool with the timeline mentioned, where all the references related here have been incorporated. Unlike other survey papers, it is the intent of this work to reinforce this initial collective effort with the web endpoint kept alive and updated.

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Section: The Era Of Practical Applications Based On P Systemsmentioning

confidence: 99%

An interactive timeline of simulators in membrane computing

et al. 2019

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“…Since the regular expressions in ex are only of the forms a and a 2 , the Q vectors are just zero vectors of lengths 4 and 2 for spiking and plasticity rules, respectively. P S = [1, 1, 1, 1] since all the spiking rules are the same, and P P = [2,1] . Nothing fires at the start of the computation, so Fi (0) S and Fi (0) P are both zero vectors.…”

Section: Definition 3 (Rule Source Matrix)mentioning

confidence: 99%

“…As elaborated in Refs. [2,6,27], the benefits of a matrix representation compared to other representations are due to the increased parallelism when performing linear algebra operations. This increased parallelism when simulating computations can benefit sequential (e.g.…”

Section: Introductionmentioning

confidence: 99%

Matrix representation and simulation algorithm of spiking neural P systems with structural plasticity

et al. 2019

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In this paper, we create a matrix representation for spiking neural P systems with structural plasticity (SNPSP, for short), taking inspiration from existing algorithms and representations for related variants. Using our matrix representation, we provide a simulation algorithm for SNPSP systems. We prove that the algorithm correctly simulates an SNPSP system: our representation and algorithm are able to capture the syntax and semantics of SNPSP systems, e.g. plasticity rules, dynamism in the synapse set. Analyses of the time and space complexity of our algorithm show that its implementation can benefit using parallel computers. Our representation and simulation algorithm can be useful when implementing SNPSP systems and related variants with a dynamic topology, in software or hardware.

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“…• Simulation of spiking neural P systems: the first simulators in this regard have been initiated by Cabarle et al [1,2]. The implemented algorithm uses a matrix representation of the model, introduced by Zeng et al [29].…”

Section: Related Simulatorsmentioning

confidence: 99%

Simulating P Systems on GPU Devices: A Survey

Amor

García-Quismondo

Macías-Ramos

et al. 2015

Fundamenta Informaticae

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P systems have been proven to be useful as modeling tools in many fields, such as Systems Biology and Ecological Modeling. For such applications, the acceleration of P system simulation is often desired, given the computational needs derived from these kinds of models. One promising solution is to implement the inherent parallelism of P systems on platforms with parallel architectures.In this respect, GPU computing proved to be an alternative to more classic approaches in Parallel Computing. It provides a low cost, and a manycore platform with a high level of parallelism. The GPU has been already employed to speedup the simulation of P systems.In this paper, we look over the available parallel P systems simulators on the GPU, with special emphasis on those included in the PMCGPU project, and analyze some useful guidelines for future implementations and developments.

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An Improved GPU Simulator for Spiking Neural P Systems

Cited by 14 publications

References 10 publications

An interactive timeline of simulators in membrane computing

An interactive timeline of simulators in membrane computing

Matrix representation and simulation algorithm of spiking neural P systems with structural plasticity

Simulating P Systems on GPU Devices: A Survey

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