A Preliminary Study on Accelerating Simulation Optimization with GPU Implementation
Fuente:
arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913319763312640 |
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| author | He, Jinghai Liu, Haoyu Wu, Yuhang Zheng, Zeyu Zhu, Tingyu |
| author_facet | He, Jinghai Liu, Haoyu Wu, Yuhang Zheng, Zeyu Zhu, Tingyu |
| contents | We provide a preliminary study on utilizing GPU (Graphics Processing Unit) to accelerate computation for three simulation optimization tasks with either first-order or second-order algorithms. Compared to the implementation using only CPU (Central Processing Unit), the GPU implementation benefits from computational advantages of parallel processing for large-scale matrices and vectors operations. Numerical experiments demonstrate computational advantages of utilizing GPU implementation in simulation optimization problems, and show that such advantage comparatively further increase as the problem scale increases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_11631 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Preliminary Study on Accelerating Simulation Optimization with GPU Implementation He, Jinghai Liu, Haoyu Wu, Yuhang Zheng, Zeyu Zhu, Tingyu Distributed, Parallel, and Cluster Computing We provide a preliminary study on utilizing GPU (Graphics Processing Unit) to accelerate computation for three simulation optimization tasks with either first-order or second-order algorithms. Compared to the implementation using only CPU (Central Processing Unit), the GPU implementation benefits from computational advantages of parallel processing for large-scale matrices and vectors operations. Numerical experiments demonstrate computational advantages of utilizing GPU implementation in simulation optimization problems, and show that such advantage comparatively further increase as the problem scale increases. |
| title | A Preliminary Study on Accelerating Simulation Optimization with GPU Implementation |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2404.11631 |