CuSNP: Spiking Neural P Systems Simulators in CUDA

Abstract

Spiking neural P systems (in short, SN P systems) are models of computation inspired by biological neurons. In this work, we report our ongoing e orts to improve simulators for SN P systems. CuSNP is a project involving sequential and parallel simulators, and in this work we include a PLingua le parser. The PLingua le parser is for ease of use when performing simulations to be executed either in the CPU or in CUDA graphics processing units (in short, GPUs). Our results also include a comparison and analysis of the simulator we developed by simulating two types of parallel soring networks: generalized and bitonic. At present, our GPU simulator is better suited on the former type based on the pro ling of our GPU kernel functions, i.e. our GPU simulators run up to 50 faster than the sequential simulator but simulations of bitonic networks run slightly slower than generalized networks. We also implemented an algorithm based on nite automata to allow more forms of regular expressions in the simulated SN P systems.