Simulation of Spiking Neural P Systems with Sparse Matrix-Vector Operations

TitleSimulation of Spiking Neural P Systems with Sparse Matrix-Vector Operations
Publication TypeJournal Papers
Year of Publication2021
AuthorsMartínez-del-Amor, M. A., Orellana-Martín D., Pérez-Hurtado I., Cabarle F. G. C., & Adorna H. N.
Journal TitleProcesses
Volume9
Abstract

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.

URLhttps://doi.org/10.3390/pr9040690
Issue4
Impact Factor

2.753

Ranking

58/143, Q2, ENGINEERING, CHEMICAL (2019)

ISSN Number2227-9717
DOI10.3390/pr9040690