Expérimentation du principe de délégation GPU pour la simulation multiagent. Les boids de Reynolds comme cas d'étude

Abstract

General-Purpose Computing on Graphics Processing Units (GPGPU) allows to extend the scalability and performances of Multi-Agent Based Simulations (MABS). However, GPGPU requires the underlying program to be compliant with the specific architecture of GPU devices, which is very constraining. In this context, the GPU Environmental Delegation of Agent Perceptions principle has been proposed to ease the use of GPGPU for MABS. The idea is to identify in the model some computations which can be transformed into environmental dynamics and then translated into GPU modules. In this paper, we further trial this principle by testing its feasibility and genericness on a classic ABM, namely Reynolds's boids. The paper then shows that applying GPU delegation not only speeds up boids simulations but also produces an ABM which is easy to understand, thanks to a clear separation of concerns.