Artículo
Simulation of Spiking Neural P Systems with Sparse Matrix-Vector Operations
Autor/es | Martínez del Amor, Miguel Ángel
Orellana Martín, David Pérez Hurtado de Mendoza, Ignacio Cabarle, Francis George C. Adorna, Henry N. |
Departamento | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial |
Fecha de publicación | 2021 |
Fecha de depósito | 2021-06-17 |
Publicado en |
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Resumen | To date, parallel simulation algorithms for spiking neural P (SNP) systems are based on
a matrix representation. This way, the simulation is implemented with linear algebra operations,
which can be easily parallelized ... To date, parallel simulation algorithms for spiking neural P (SNP) systems are based on a matrix representation. This way, the simulation is implemented with linear algebra operations, which can be easily parallelized on high performance computing platforms such as GPUs. Although it has been convenient for the first generation of GPU-based simulators, such as CuSNP, there are some bottlenecks to sort out. For example, the proposed matrix representations of SNP systems lead to very sparse matrices, where the majority of values are zero. It is known that sparse matrices can compromise the performance of algorithms since they involve a waste of memory and time. This problem has been extensively studied in the literature of parallel computing. In this paper, we analyze some of these ideas and apply them to represent some variants of SNP systems. We also provide a new simulation algorithm based on a novel compressed representation for sparse matrices. We also conclude which SNP system variant better suits our new compressed matrix representation. |
Agencias financiadoras | Ministerio de Ciencia e Innovación (MICIN). España |
Identificador del proyecto | TIN2017-89842-P |
Cita | Martínez del Amor, M.Á., Orellana Martín, D., Pérez Hurtado de Mendoza, I., Cabarle, F.G.C. y Adorna, H.N. (2021). Simulation of Spiking Neural P Systems with Sparse Matrix-Vector Operations. Processes, 9 (4) |
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