Environment as support for simplification, reuse and integration of processes in spatial MAS

Payet, Denis; Courdier, Rémy; Nicolas, Sébastien; Ralambondrainy, Tiana

doi:10.1109/iri.2006.252400

Cited by 15 publications

(6 citation statements)

References 5 publications

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“…Examples of this trend are EASS (Environment As Active Support for Simulation) [2], IODA (Interaction Oriented Design of Agent simulations) [11], the environment-centered approach for MABS proposed in [19] and artifact approach [21].…”

Section: The Gpu Environmental Delegation Principlementioning

confidence: 99%

Overview of Case Studies on Adapting MABS Models to GPU Programming

Hermellin

Michel

2016

Highlights of Practical Applications of Scalable Multi-Agent Systems. The PAAMS Collection

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Abstract. General-Purpose Computing on Graphics Units (GPGPU) is today recognized as a practical and efficient way of accelerating software procedures that require a lot of computing resources. However, using this technology in the context of Multi-Agent Based Simulation (MABS) appears to be difficult because GPGPU relies on a very specific programming approach for which MABS models are not naturally adapted. This paper discusses practical results from several works we have done on adapting and developing different MABS models using GPU programming. Especially, studying how GPGPU could be used in the scope of MABS, our main motivation is not only to speed up MABS but also to provide the MABS community with a general approach to GPU programming, which could be used on a wide variety of agent-based models. So, this paper first summarizes all the use cases that we have considered so far and then focuses on identifying which parts of the development process could be generalized.

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Section: The Gpu Environmental Delegation Principlementioning

confidence: 99%

Overview of Case Studies on Adapting MABS Models to GPU Programming

Hermellin

Michel

2016

Highlights of Practical Applications of Scalable Multi-Agent Systems. The PAAMS Collection

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“…IODA (Interaction Oriented Design of Agent simulations) [4] is centered on the notion of interaction and considers that agent behaviors can be described in a abstract way as a rule called interaction. [12] proposes to reduce the complexity of the models by using an environment-centered approach: Some agent interaction patterns are modeled as environmental dynamics to ease the reusability and the integration of various agent processes.…”

Section: Converting Agent Perceptions In Environmental Dynamicsmentioning

confidence: 99%

GPU Environmental Delegation of Agent Perceptions: Application to Reynolds’s Boids

Hermellin

Michel

2016

Multi-Agent Based Simulation XVI

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Abstract. Using Multi-Agent Based Simulation (MABS), computing resources requirements often limit the extent to which a model could be experimented with. Regarding this issue, some research works propose to use the General-Purpose Computing on Graphics Processing Units (GPGPU) technology. GPGPU allows to use the massively parallel architecture of graphic cards to perform general-purpose computing with huge speedups. Still, GPGPU requires the underlying program to be compliant with the specific architecture of GPU devices, which is very constraining. Especially, it turns out that doing MABS using GPGPU is very challenging because converting Agent Based Models (ABM) accordingly is a very difficult task. In this context, the GPU Environmental Delegation of Agent Perceptions principle has been proposed to ease the use of GPGPU for MABS. This principle consists in making a clear separation between the agent behaviors, managed by the CPU, and environmental dynamics, handled by the GPU. For now, this principle has shown good results, but only on one single case study. In this paper, we further trial this principle by testing its feasibility and genericness on a classic ABM, namely Reynolds's boids. To this end, we first review existing boids implementations to then propose our own benchmark model. 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.

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“…Arborant un point de vue environnement-centrée, la délégation GPU doit être rattacher à d'autres travaux tels que l'approche EASS (Environment As Active Support for Simulation) (Badeig, Balbo, 2012) qui vise à renforcer le rôle de l'environnement en lui déléguant la politique d'ordonnancement ainsi qu'un système de filtrage des perceptions, IODA (Interaction Oriented Design of Agent simulations) (Kubera et al, 2011) est quant à elle centrée sur la notion d'interaction et considère que tout comportement réalisable par des agents est décrit de façon abstraite (c'est-à-dire en exprimant ce qu'il a de général) sous la forme d'une règle appelée interaction. Enfin, (Payet et al, 2006) propose une réduction de la complexité des modèles en se basant sur une approche environnementcentrée : l'environnement devient alors un espace d'échange dédié à l'exécution de dynamiques visant à faciliter la réutilisabilité et l'intégration des différents processus des agents. Dans un contexte plus général, l'approche des artefacts intègre dans l'environnement un ensemble d'entités dynamiques structurant les ressources et les outils que les agents vont pouvoir utiliser et partager (Ricci et al, 2011 ;Viroli et al, 2006).…”

Section: Relation Avec D'autres Travaux De Rechercheunclassified

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

Hermellin¹,

Michel²

2016

Revue d'intelligence artificielle

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To cite this version:Emmanuel Hermellin, Fabien Michel. Expérimentation du principe de délégation GPU pour la simulation multiagent : Les boids de Reynolds comme cas d'étude. Revue des Sciences et Technologies de l'Information -Série RIA :RÉSUMÉ. L'utilisation du GPGPU (General-Purpose Computing on Graphics Processing Units) pour la simulation multiagent permet d'améliorer les performances des modèles et lève ainsi une partie des contraintes liées au passage à l'échelle. Cependant, adapter un modèle pour qu'il utilise le GPU est une tâche complexe car le GPGPU repose sur une programmation extrêmement spécifique et contraignante. C'est dans ce contexte que la délégation GPU des perceptions agents a été proposée. L'idée derrière ce principe est d'identifier dans le modèle des calculs qui peuvent être transformés en dynamiques environnementales afin d'être calculés par des modules GPU. Il a été appliqué sur un cas d'étude et a montré de bons résultats en termes de performances et de conception. Dans cet article, nous proposons de mettre à l'épreuve la faisabilité et la généricité de cette approche en appliquant le principe de délégation GPU sur un modèle agent classique : les boids de Reynolds. Nous montrons que le principe de délégation offre des résultats intéressants au niveau des performances mais aussi d'un point de vue conceptuel (généricité, accessibilité, etc.).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. MOTS-CLÉS : Simulation multiagent, GPGPU, Boids de Reynolds, CUDA.

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Environment as support for simplification, reuse and integration of processes in spatial MAS

Cited by 15 publications

References 5 publications

Overview of Case Studies on Adapting MABS Models to GPU Programming

Overview of Case Studies on Adapting MABS Models to GPU Programming

GPU Environmental Delegation of Agent Perceptions: Application to Reynolds’s Boids

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

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