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Improving GPU Simulations of Spiking Neural P Systems

 

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Opened Access Improving GPU Simulations of Spiking Neural P Systems
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Author: Cabarle, Francis George C.
Adorna, Henry N.
Martínez del Amor, Miguel Ángel
Pérez Jiménez, Mario de Jesús
Department: Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial
Date: 2012
Published in: Romanian Journal of Information Science and Technology, 15 (1), 5-20.
Document type: Article
Abstract: In this work we present further extensions and improvements of a Spiking Neural P system (for short, SNP systems) simulator on graphics processing units (for short, GPUs). Using previous results on representing SNP system computations using linear algebra, we analyze and implement a compu- tation simulation algorithm on the GPU. A two-level parallelism is introduced for the computation simulations. We also present a set of benchmark SNP sys- tems to stress test the simulation and show the increased performance obtained using GPUs over conventional CPUs. For a 16 neuron benchmark SNP system with 65536 nondeterministic rule selection choices, we report a 2.31 speedup of the GPU-based simulations over CPU-based simulations.
Cite: Cabarle, F.G.C., Adorna, H.N., Martínez del Amor, M.Á. y Pérez Jiménez, M.d.J. (2012). Improving GPU Simulations of Spiking Neural P Systems. Romanian Journal of Information Science and Technology, 15 (1), 5-20.
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URI: https://hdl.handle.net/11441/79693

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