一种基于CUDA的大数据量地理加权回归并行加速算法

doi:10.13474/j.cnki.11-2246.2020.0379

测绘通报 ›› 2020, Vol. 0 ›› Issue (12): 1-5.doi: 10.13474/j.cnki.11-2246.2020.0379

• 学术研究 • 下一篇

一种基于CUDA的大数据量地理加权回归并行加速算法

刘振涛^1,2, 杨毅³, 王东超³, 谢晓尧⁴

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

收稿日期:2020-06-03 修回日期:2020-10-28 发布日期:2021-01-06
通讯作者: 杨毅。E-mail:yangyi@jou.edu.cn E-mail:yangyi@jou.edu.cn
作者简介:刘振涛(1977-),男,博士生,高级工程师,主要研究方向为计算机软件与理论。E-mail:19415272@qq.com
基金资助:
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）

A CUDA-based parallel accelerating geographically weighted regression algorithm for big data

LIU Zhentao^1,2, YANG Yi³, WANG Dongchao³, XIE Xiaoyao⁴

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

Received:2020-06-03 Revised:2020-10-28 Published:2021-01-06

摘要/Abstract

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。

关键词: 地理加权回归, CUDA, GPU, 并行加速, 大数据

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.

Key words: GWR, CUDA, GPU, parallel acceleration, big data

中图分类号:

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.

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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一种基于CUDA的大数据量地理加权回归并行加速算法

A CUDA-based parallel accelerating geographically weighted regression algorithm for big data

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