A GPU-Based Multi-agent System for Real-Time Simulations

Vigueras, Guillermo; Orduña, Juan M.; Lozano, Miguel

doi:10.1007/978-3-642-12384-9_3

Cited by 9 publications

(13 citation statements)

References 8 publications

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“…On other hand, we have implemented a distributed server for crowd simulations that can take advantage of the computational power of current Graphics Processor Units (GPUs) [19]. Since the consistency maintained by the distributed server is the critical path in the system, we have implemented on the GPU this part of the server.…”

Section: Remaining Objectivesmentioning

confidence: 99%

Performance improvements of real-time crowd simulations

Vigueras

Orduña

Lozano

2010

2010 IEEE International Symposium on Parallel &Amp; Distributed Processing, Workshops and PHD Forum (IPDPSW)

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The current challenge for crowd simulations is the design and development of a scalable system that is capable of simulating the individual behavior of millions of complex agents populating large scale virtual worlds with a good frame rate. In order to overcome this challenge, this thesis proposes different improvements for crowd simulations. Concretely, we propose a distributed software architecture that can take advantage of the existing distributed and multicore architectures. In turn, the use of these distributed architectures requires partitioning strategies and workload balancing techniques for distributed crowd simulations. Also, these architectures allow the use of GPUs not only for rendering images but also for computing purposes. Finally, the design and implementation of distributed visual clients is another research topic that can help to overcome this challenge. Description of the ProblemCrowd simulation has become an essential tool for many virtual environment applications, and the extensive use of high quality virtual crowds is crucial for many virtual environment applications in education, training, and entertainment [1]. Crowd simulation can be considered as a special case of Virtual Environments where the avatars are intelligent agents instead of user-driven entities. Each of these agentbased entities can have its own goals, knowledge and behavior [2].On the one hand, crowd simulations must focus on rendering visually plausible images of the environment, requiring a high computational cost. On the other hand, complex agents must have autonomous behaviors, greatly increasing the computational cost as well. The sum of these requirements results in a computational cost that exponentially increases with the numbers of agents in the system, requiring a scalable design that can support huge amounts of agents (of different orders of magnitude) by simply adding more hardware. Thus, some proposals tackle crowd simulations as a particle system with different levels of details (eg:impostors) in order to reduce the computational cost [3] due to the graphical quality. Also, several proposals have been made to provide efficient and autonomous behaviors to crowd simulations [4]. The current challenge for these applications is the design and development of a scalable system that is capable of simulating the individual behavior of millions of complex agents populating large scale virtual worlds with a good frame rate.

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Section: Remaining Objectivesmentioning

confidence: 99%

Performance improvements of real-time crowd simulations

Vigueras

Orduña

Lozano

2010

2010 IEEE International Symposium on Parallel &Amp; Distributed Processing, Workshops and PHD Forum (IPDPSW)

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“…The hybrid approach (2) allows to use jointly the CPU and GPU and thus has two major advantages. Firstly, it brings more flexibility because one can choose what is going to be executed on the GPU, thus providing greater accessibility to the developed tools (as clearly shown in [6] [5], [16], [18]). Secondly, as hybrid systems are modular by design, they make it possible to use agents with complex and heterogeneous architectures.…”

Section: Mabs and Gpgpumentioning

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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“…L'approche hybride (2) est certe moins performante qu'une approche tout-sur-GPU (1) cependant le fait qu'elle autorise une utilisation conjointe du CPU et du GPU apporte plusieurs avantages majeurs. En permettant de choisir ce qui va être exécuté sur le GPU et par le CPU, l'approche hybride se veut plus flexible et ouverte aux autres technologies augmentant ainsi l'accessibilité des outils développés (ce qui est clairement visible dans (Laville et al, 2012 ;Sano et al, 2014 ;Vigueras et al, 2010)). De plus, cette approche est plus modulaire par sa conception, rendant ainsi possible l'utilisation d'architectures agents plus complexes et hétérogènes.…”

Section: Simulations Multiagents Et Gpgpuunclassified

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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A GPU-Based Multi-agent System for Real-Time Simulations

Cited by 9 publications

References 8 publications

Performance improvements of real-time crowd simulations

Performance improvements of real-time crowd simulations

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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