基于Delaunay三角网的克里金并行算法优化
Optimization of Kriging Parallel Algorithm Based on Delaunay Triangulation Network
陈国军 1李子祥 1付云鹏 1李震烁1
作者信息
- 1. 中国石油大学(华东)计算科学与技术学院,青岛 266580
- 折叠
摘要
当采样点数据量较大时,可以采用Delaunay三角剖分建立三角网来使用局部邻域采样点进行克里金插值.但是该算法需要对每个插值点拟合半变异函数,插值点规模大时造成巨大开销.为此,本文提出了一种以三角形为单位拟合半变异函数的克里金插值方法,采用CPU-GPU负载均衡将部分计算优化,充分考虑不均匀样本对克里金插值效果的影响.结果表明,本文算法能够保证不均匀样本集的插值效果,提升了计算性能且能够保证较高的精度.
Abstract
Under a large data amount of sampling points,Delaunay triangulation can be adopted to establish a triangulation network and then employ local neighborhood sampling points for Kriging interpolation.However,this algorithm requires fitting a semi-variogram to each interpolation point,which incurs significant overhead in the condition of a large interpolation point scale.Therefore,this study proposes a Kriging interpolation method that fits the semi-variogram on a triangular basis.Additionally,it utilizes CPU-GPU load balancing to optimize some calculations and fully considers the influence of non-uniform samples on the Kriging interpolation effect.The results show that the proposed algorithm can ensure the interpolation effect of non-uniform sample sets,improve computational performance,and ensure high accuracy.
关键词
负载均衡/克里金插值/邻域搜索/并行计算Key words
load balancing/Kriging interpolation/neighborhood search/parallel computing引用本文复制引用
出版年
2024
NETL
NSTL
维普
万方数据