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Sparse-matrix Representation of Spiking Neural P Systems for GPUs

Opened Access Sparse-matrix Representation of Spiking Neural P Systems for GPUs
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Autor: Martínez del Amor, Miguel Ángel
Orellana Martín, David
Cabarle, Francis George C.
Pérez Jiménez, Mario de Jesús
Adorna, Henry N.
Departamento: Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial
Fecha: 2017
Publicado en: BWMC 2017: 15th Brainstorming Week on Membrane Computing (2017), p 161-170
ISBN/ISSN: 978-84-946316-9-6
Tipo de documento: Ponencia
Resumen: Current parallel simulation algorithms for Spiking Neural P (SNP) systems are based on a matrix representation. This helps to harness the inherent parallelism in algebraic operations, such as vector-matrix multiplication. Although it has been convenient for the rst parallel simulators running on Graphics Processing Units (GPUs), such as CuSNP, there are some bottlenecks to cope with. For example, matrix representation of SNP systems with a low-connectivity-degree graph lead to sparse matrices, i.e. containing more zeros than actual values. Having to deal with sparse matrices downgrades the performance of the simulators because of wasting memory and time. However, sparse matrices is a known problem on parallel computing with GPUs, and several solutions and algorithms are available in the literature. In this paper, we brie y analyse some of these ideas and apply them to represent some variants of SNP systems. We also conclude which variant better suit a sparse-matrix repre...
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Cita: Martínez del Amor, M.Á., Orellana Martín, D., Cabarle, F.G.C., Pérez Jiménez, M.d.J. y Adorna, H.N. (2017). Sparse-matrix Representation of Spiking Neural P Systems for GPUs. En BWMC 2017: 15th Brainstorming Week on Membrane Computing (161-170), Sevilla, España: Fenix Editora.
Tamaño: 319.2Kb
Formato: PDF

URI: http://hdl.handle.net/11441/67895

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