GPU-Accelerated Simulation for Class of Multi-Agent Based Models

Authors

Zhao Yuan, 1. Guangdong College of Industry & Commerce, Guangzhou 510510, China;;2. College of Informatics, South China Agricultural University, Guangzhou 510642, China;;3. Guangdong Province Key Laboratory of Land Use and Consolidation, Guangzhou 510642, China;;4. Key Laboratory of the Ministry of Land and Resources for Construction Land Transformation, Guangzhou 510642, China;
Jiachang Cheng, 2. College of Informatics, South China Agricultural University, Guangzhou 510642, China;;
Wang Lu, 2. College of Informatics, South China Agricultural University, Guangzhou 510642, China;;3. Guangdong Province Key Laboratory of Land Use and Consolidation, Guangzhou 510642, China;;4. Key Laboratory of the Ministry of Land and Resources for Construction Land Transformation, Guangzhou 510642, China;
Yueming Hu, 2. College of Informatics, South China Agricultural University, Guangzhou 510642, China;;3. Guangdong Province Key Laboratory of Land Use and Consolidation, Guangzhou 510642, China;;4. Key Laboratory of the Ministry of Land and Resources for Construction Land Transformation, Guangzhou 510642, China;

Abstract

Abstract: A parallel agent-based model of Von Thünen Model was proposed driven by graphics processing units (GPUs). The Von Thünen Model often involved the simulation of large numbers of geographically located individual decision-makers and a massive number of individual-level interactions. This simulation required substantial computational power. GPU-enabled computing resources provided a massively parallel processing platform based on a fine-grained shared memory paradigm. This massively parallel processing platform held considerable promise for meeting the computing requirement of agent-based models of spatial problems. A dynamic relationship table rebuilding method was proposed to enable the use of GPUs for parallel agent-based modeling of the spatial Von Thünen Model. The key algorithm played an important role in best exploiting high-performance resources in GPUs for large-scale spatial simulation. Experiments conducted to examine computing performance show that GPUs provide a computationally efficient alternative to traditional parallel computing architectures and substantially accelerate agent-based models in large-scale spatial space.

Recommended Citation

Yuan, Zhao; Cheng, Jiachang; Lu, Wang; and Hu, Yueming (2020) "GPU-Accelerated Simulation for Class of Multi-Agent Based Models," Journal of System Simulation: Vol. 27: Iss. 2, Article 24.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol27/iss2/24

First Page

396

Revised Date

2014-04-13

DOI Link

https://doi.org/

Last Page

403

CLC

TP391.9

Recommended Citation

Zhao Yuan, Cheng Jiachang, Wang Lu, Hu Yueming. GPU-Accelerated Simulation for Class of Multi-Agent Based Models[J]. Journal of System Simulation, 2015, 27(2): 396-403.