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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.13382 |
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| _version_ | 1866913661865426944 |
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| author | Sheng, Zhang Duan, Lishu Jiang, Hanbo |
| author_facet | Sheng, Zhang Duan, Lishu Jiang, Hanbo |
| contents | This study presents a reconstruction of the Gaussian Beam Tracing solution using CUDA, with a particular focus on the utilisation of GPU acceleration as a means of overcoming the performance limitations of traditional CPU algorithms in complex acoustic simulations. The algorithm is implemented and optimised on the NVIDIA RTX A6000 GPU, resulting in a notable enhancement in the performance of the Gaussian Beam Summation (GBS) process. In particular, the GPU-accelerated GBS algorithm demonstrated a significant enhancement in performance, reaching up to 790 times faster in city enviroment and 188 times faster in open plane enviroment compared to the original CPU-based program. To address the challenges of acceleration, the study introduce innovative solutions for handling irregular loops and GPU memory limitations, ensuring the efficient processing of large quantities of rays beyond the GPU's single-process capacity. Furthermore, this work established performance evaluation strategies crucial for analysing and reconstructing similar algorithms. Additionally, the study explored future directions for further accelerating the algorithm, laying the groundwork for ongoing improvements. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_13382 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Accelerating Gaussian beam tracing method with dynamic parallelism on graphics processing units Sheng, Zhang Duan, Lishu Jiang, Hanbo Performance Distributed, Parallel, and Cluster Computing This study presents a reconstruction of the Gaussian Beam Tracing solution using CUDA, with a particular focus on the utilisation of GPU acceleration as a means of overcoming the performance limitations of traditional CPU algorithms in complex acoustic simulations. The algorithm is implemented and optimised on the NVIDIA RTX A6000 GPU, resulting in a notable enhancement in the performance of the Gaussian Beam Summation (GBS) process. In particular, the GPU-accelerated GBS algorithm demonstrated a significant enhancement in performance, reaching up to 790 times faster in city enviroment and 188 times faster in open plane enviroment compared to the original CPU-based program. To address the challenges of acceleration, the study introduce innovative solutions for handling irregular loops and GPU memory limitations, ensuring the efficient processing of large quantities of rays beyond the GPU's single-process capacity. Furthermore, this work established performance evaluation strategies crucial for analysing and reconstructing similar algorithms. Additionally, the study explored future directions for further accelerating the algorithm, laying the groundwork for ongoing improvements. |
| title | Accelerating Gaussian beam tracing method with dynamic parallelism on graphics processing units |
| topic | Performance Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2501.13382 |