| 一种基于CUDA的大数据量地理加权回归并行加速算法 |
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| 引用本文： | 刘振涛,杨毅,王东超,谢晓尧.一种基于CUDA的大数据量地理加权回归并行加速算法[J].测绘通报,2020,0(12):1-5. |
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| 作者姓名： | 刘振涛 杨毅 王东超 谢晓尧 |
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| 作者单位： | 1. 贵州大学计算机科学与技术学院, 贵州 贵阳 550025;2. 贵州省科技信息中心, 贵州 贵阳 550002;3. 江苏海洋大学测绘与海洋信息学院, 江苏 连云港 222005;4. 贵州师范大学贵州省信息与计算科学重点实验室, 贵州 贵阳 550001 |
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| 基金项目： | 科技资源平台建设计划贵州省科技创新云平台建设（黔科合计KF【2015】4002） |
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| 摘 要： | 针对传统地理加权回归（GWR）在大数据量计算中存在的计算效率低、内存占用大、数据规模受限等问题，本文提出了快速并行地理加权回归（FPGWR）算法，基于英伟达CUDA架构实现了GWR的并行加速，将串行过程分解为并行的独立回归计算模块，同时优化了内存使用模型，提高了算法的运行速度。对比FPGWR和传统GWR在不同数量级模拟数据上和真实数据上的运行速度，结果显示，FPGWR能够支持更大规模的样本量计算并有效提升运行效率，数据量越大加速效果越显著。
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| 关 键 词： | 地理加权回归 CUDA GPU 并行加速 大数据 |
| 收稿时间： | 2020-06-03 |
| 修稿时间： | 2020-10-28 |
A CUDA-based parallel accelerating geographically weighted regression algorithm for big data |
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| LIU Zhentao,YANG Yi,WANG Dongchao,XIE Xiaoyao.A CUDA-based parallel accelerating geographically weighted regression algorithm for big data[J].Bulletin of Surveying and Mapping,2020,0(12):1-5. |
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| Authors: | LIU Zhentao YANG Yi WANG Dongchao XIE Xiaoyao |
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| Institution: | 1. College of Computer Science and Technology, Guizhou University, Guiyang 550025, China;2. Guizhou Science Technology Information Center, Guiyang 550002, China;3. School of Geomatics and Marine Information, Jiangsu Ocean University, Lianyungang 222005, China;4. Key Laboratory of Information and Computing Science Guizhou Province, Guizhou Normal University, Guiyang 550001, China |
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| Abstract: | In order to improve calculation speed and reduce memory consumption in traditional GWR, this paper proposes a fast parallel geographically weighted regression (FPGWR) algorithm, which bases on the NVIDIA computed unified device architecture (CUDA) to achieve parallel acceleration of GWR. The FPGWR algorithm finely decomposes the serial process into parallel independent computing modules and optimizes the memory usage model to increase the speed of GWR algorithm. This paper compares the calculation speed of FPGWR and traditional GWR towards simulated and real data. Results show that FPGWR can support a larger sample calculation and effectively increase the calculation speed. In addition, the larger the amount of data, the faster the algorithm presents. |
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| Keywords: | GWR CUDA GPU parallel acceleration big data |
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