Large Scale Multi-GPU Based Parallel Traffic Simulation for Accelerated Traffic Assignment and Propagation
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909359453241344 |
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| author | Jiang, Xuan Sengupta, Raja Demmel, James Williams, Samuel |
| author_facet | Jiang, Xuan Sengupta, Raja Demmel, James Williams, Samuel |
| contents | Traffic propagation simulation is crucial for urban planning, enabling congestion analysis, travel time estimation, and route optimization. Traditional micro-simulation frameworks are limited to main roads due to the complexity of urban mobility and large-scale data. We introduce the Large Scale Multi-GPU Parallel Computing based Regional Scale Traffic Simulation Framework (LPSim), a scalable tool that leverages GPU parallel computing to simulate extensive traffic networks with high fidelity and reduced computation time. LPSim performs millions of vehicle dynamics simulations simultaneously, outperforming CPU-based methods. It can complete simulations of 2.82 million trips in 6.28 minutes using a single GPU, and 9.01 million trips in 21.16 minutes on dual GPUs. LPSim is also tested on dual NVIDIA A100 GPUs, achieving simulations about 113 times faster than traditional CPU methods. This demonstrates its scalability and efficiency for large-scale applications, making LPSim a valuable resource for researchers and planners. Code: https://github.com/Xuan-1998/LPSim |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_08496 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Large Scale Multi-GPU Based Parallel Traffic Simulation for Accelerated Traffic Assignment and Propagation Jiang, Xuan Sengupta, Raja Demmel, James Williams, Samuel Distributed, Parallel, and Cluster Computing Traffic propagation simulation is crucial for urban planning, enabling congestion analysis, travel time estimation, and route optimization. Traditional micro-simulation frameworks are limited to main roads due to the complexity of urban mobility and large-scale data. We introduce the Large Scale Multi-GPU Parallel Computing based Regional Scale Traffic Simulation Framework (LPSim), a scalable tool that leverages GPU parallel computing to simulate extensive traffic networks with high fidelity and reduced computation time. LPSim performs millions of vehicle dynamics simulations simultaneously, outperforming CPU-based methods. It can complete simulations of 2.82 million trips in 6.28 minutes using a single GPU, and 9.01 million trips in 21.16 minutes on dual GPUs. LPSim is also tested on dual NVIDIA A100 GPUs, achieving simulations about 113 times faster than traditional CPU methods. This demonstrates its scalability and efficiency for large-scale applications, making LPSim a valuable resource for researchers and planners. Code: https://github.com/Xuan-1998/LPSim |
| title | Large Scale Multi-GPU Based Parallel Traffic Simulation for Accelerated Traffic Assignment and Propagation |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2406.08496 |