Producer-Consumer Model for Massively Parallel Zero-Sum Games on the GPU

Bleiweiss, Avi

doi:10.2316/p.2011.744-073

Cited by 4 publications

(4 citation statements)

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“… Agents are autonomous and partially independent from each other  Agents can interact and communicate with other agents  No agent has a full global view of the system  No agent is designated as controlling Multi-agent systems have been the subject of study for several decades and have been applied to a variety of problems. In the field of computer graphics, they have been used in AI-based game elements such as navigation and motion planning for non-player characters (Bleiweiss, 2009), as well as the design of particle systems (Xiao et al, 2011), crowd simulations and shape constrained animations (Chen et al, 2019).…”

Section: Related Workmentioning

confidence: 99%

An agent-based paradigm for virtual modeling

Conesa

Camba

Aranda

et al. 2022

Expert Systems with Applications

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Section: Related Workmentioning

confidence: 99%

An agent-based paradigm for virtual modeling

Conesa

Camba

Aranda

et al. 2022

Expert Systems with Applications

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“…Leskinen et al [12] used GPU processing on Nash games for evolutionary optimization. Bleiweiss [13] exploited the massively parallel GPU architecture to model producer-consumer zero-sum games.…”

Section: A Related Workmentioning

confidence: 99%

Computing Nash equilibria in bimatrix games: GPU-based parallel support enumeration

Rampersaud

Mashayekhy

Grosu

2012

2012 IEEE 31st International Performance Computing and Communications Conference (IPCCC)

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Abstract-Computing Nash equilibria is a very important problem in strategic analysis of markets, conflicts and resource allocation. Unfortunately, computing these equilibria even for moderately sized games is computationally expensive. To obtain faster execution times it is essential to exploit the available parallelism offered by the currently available massively parallel architectures. To address this issue, we design a GPU-based parallel support enumeration algorithm for computing Nash equilibria in bimatrix games. The algorithm is based on a new parallelization method which achieves high degrees of parallelism suitable for massively parallel GPU architectures. We perform extensive experiments to characterize the performance of the proposed algorithm. The algorithm achieves significant speedups relative to the OpenMP-based parallel implementation of the support enumeration method running on conventional multicore machines.

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“…The GPU-based implementation of game tree search has previously been reported by Bleiweiss, but he demonstrated significant speedups only for practically limited case of thousands of simultaneously running games [1]. Another report shows good acceleration results with the exhaustive minimax tree search, which represents the foundation of the alpha-beta algorithm [17].…”

Section: Introductionmentioning

confidence: 95%

Parallel Alpha-Beta Algorithm on the GPU

Strnad

Guid

2011

CIT

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In the paper we present the parallel implementation of the alpha-beta algorithm running on the graphics processing unit (GPU). We compare the speed of the parallel player with the standard serial one using the game of reversi with boards of different sizes. We show that for small boards the level of available parallelism is insufficient for efficient GPU utilization, but for larger boards substantial speed-ups can be achieved on the GPU. The results indicate that the GPU-based alpha-beta implementation would be advantageous for similar games of higher computational complexity (e.g. hex and go) in their standard form.

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Producer-Consumer Model for Massively Parallel Zero-Sum Games on the GPU

Cited by 4 publications

References 0 publications

An agent-based paradigm for virtual modeling

An agent-based paradigm for virtual modeling

Computing Nash equilibria in bimatrix games: GPU-based parallel support enumeration

Parallel Alpha-Beta Algorithm on the GPU

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