Simulating Spiking Neural P systems without delays using GPUs

TL;DR

This paper demonstrates how to efficiently simulate spiking neural P systems on GPUs by leveraging their inherent parallelism, enabling faster and scalable computations for biologically inspired neural models.

Contribution

It introduces a GPU-based simulation method for SNP systems, utilizing matrix representations to exploit parallel processing capabilities.

Findings

GPU simulation significantly accelerates SNP system computations

Matrix representation facilitates efficient parallel implementation

Successful simulation of a system generating all natural numbers except 1

Abstract

We present in this paper our work regarding simulating a type of P system known as a spiking neural P system (SNP system) using graphics processing units (GPUs). GPUs, because of their architectural optimization for parallel computations, are well-suited for highly parallelizable problems. Due to the advent of general purpose GPU computing in recent years, GPUs are not limited to graphics and video processing alone, but include computationally intensive scientific and mathematical applications as well. Moreover P systems, including SNP systems, are inherently and maximally parallel computing models whose inspirations are taken from the functioning and dynamics of a living cell. In particular, SNP systems try to give a modest but formal representation of a special type of cell known as the neuron and their interactions with one another. The nature of SNP systems allowed their representation as matrices, which is a crucial step in simulating them on highly parallel devices such as GPUs. The highly parallel nature of SNP systems necessitate the use of hardware intended for parallel computations. The simulation algorithms, design considerations, and implementation are presented. Finally, simulation results, observations, and analyses using an SNP system that generates all numbers in $\mathbb N$ - {1} are discussed, as well as recommendations for future work.