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research-article

GPU implementation of evolving spiking neural P systems

Authors:

Rogelio V. Gungon,

Katreen Kyle M. Hernandez,

Francis George C. Cabarle,

Ren Tristan A. de la Cruz,

Henry N. Adorna,

Miguel Á. Martínez-del-Amor,

David Orellana-Martín,

Ignacio Pérez-HurtadoAuthors Info & Claims

Volume 503, Issue C

Pages 140 - 161

https://doi.org/10.1016/j.neucom.2022.06.094

Published: 07 September 2022 Publication History

Highlights

•

A novel parallel framework for evolving spiking neural P systems.

•

GPU implementation of the genetic algorithm employed in the parallel framework.

•

Adapting CuSNP design for the efficient simulation of spiking neural P systems in the parallel framework.

•

Reporting up to 9x of speedup of the GPU implementation versus the CPU counterpart for the parallel step, and up to 3x for the overall process.

Abstract

Methods for optimizing and evolving spiking neural P systems (in short, SN P systems) have been previously developed with the use of a genetic algorithm framework. So far, these computations, both evolving and simulating, were done only sequentially. Due to the non-deterministic and parallel nature of SN P systems, it is natural to harness parallel processors in implementing its evolution and simulation. In this work, a parallel framework for the evolution of SN P Systems is presented. This is the result of extending our previous work by implementing it on a CUDA-enabled graphics processing unit and adapting CuSNP design in simulations. Using binary addition and binary subtraction with 3 different categories each as initial SN P systems, the GPU-based evolution runs up to 9x faster with respect to its CPU-based evolution counterparts. Overall, when considering the whole process, the GPU framework is up to 3 times faster than the CPU version.

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Cited By

de la Cruz RCabarle FAdorna H(2024)Steps toward a homogenization procedure for spiking neural P systemsTheoretical Computer Science10.1016/j.tcs.2023.114250981:COnline publication date: 4-Jan-2024
https://dl.acm.org/doi/10.1016/j.tcs.2023.114250
Wang LLiu XHan ZZhao Y(2024)Spiking neural P systems with neuron permeabilityNeurocomputing10.1016/j.neucom.2024.127351576:COnline publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1016/j.neucom.2024.127351
Zhang XLiu XRen QSun MZhao Y(2024)A general neural membrane computing modelInformation Sciences: an International Journal10.1016/j.ins.2024.120686672:COnline publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1016/j.ins.2024.120686
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Published In

cover image Neurocomputing

Neurocomputing Volume 503, Issue C

Sep 2022

364 pages

ISSN:0925-2312

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Elsevier Science Publishers B. V.

Netherlands

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Published: 07 September 2022

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Cited By

de la Cruz RCabarle FAdorna H(2024)Steps toward a homogenization procedure for spiking neural P systemsTheoretical Computer Science10.1016/j.tcs.2023.114250981:COnline publication date: 4-Jan-2024
https://dl.acm.org/doi/10.1016/j.tcs.2023.114250
Wang LLiu XHan ZZhao Y(2024)Spiking neural P systems with neuron permeabilityNeurocomputing10.1016/j.neucom.2024.127351576:COnline publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1016/j.neucom.2024.127351
Zhang XLiu XRen QSun MZhao Y(2024)A general neural membrane computing modelInformation Sciences: an International Journal10.1016/j.ins.2024.120686672:COnline publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1016/j.ins.2024.120686
Xue JKong DRen LSong BLiu XGong GLi DZhang H(2023)Spiking neural P system with synaptic vesicles and applications in multiple brain metastasis segmentationInformation Sciences: an International Journal10.1016/j.ins.2023.01.016625:C(620-638)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1016/j.ins.2023.01.016

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