High Performance and Scalable Simulations of a Bio-inspired Computational Model

Abstract

The Network of Polarized Evolutionary Processors (NPEP) is a rather new variant of the bio-inspired computing model called Network of Evolutionary Processors (NEP). This model, together with its variants, is able to provide theoretical feasible solutions to hard computational problems. NPEPE is a software engine able to simulate NPEP which is deployed over Giraph, an ultra-scalable platform based on the Bulk Synchronous Parallel (BSP) programming model. Rather surprisingly, the BSP model and the underlying architecture of NPEP have many common points. Moreover, these similarities are also shared with all variants in the NEP family. We take advantage of these similarities and propose an extension of NPEPE (named gNEP) that enhances it to simulate any variant of the NEP’s family. Our extended gNEP framework, presents a twofold contribution. Firstly, a flexible architecture able to extend software components in order to include other NEP models (including the seminal NEP model and new ones). Secondly, a component able to translate input configuration files representing the instance of a problem and an algorithm based on different variants of the NEP model into some suitable input files for gNEP framework. In this work, we simulate a solution to the “3-colorability” problem which is based on NPEP. We compare the results for a specific experiment using NPEPE engine and gNEP. Moreover, we show several experiments in the aim of studying, in a preliminary way, the scalability offered by gNEP to easily deploy and execute instances of problems requiring more intensive computations.