统一计算设备架构下的F-X域预测滤波并行算法  

F-X domain predictive filtering parallel algorithm based on compute unified device architecture

作　　者：杨先凤[1] 贵红军 傅春常[2] YANG Xianfeng;GUI Hongjun;FU Chunchang(School of Computer Science,Southwest Petroleum University,Chengdu Sichuan 610500,China;School of Computer Science and Technology,Southwest Minzu University,Chengdu Sichuan 610041,China)

机构地区：[1]西南石油大学计算机科学学院,成都610500 [2]西南民族大学计算机科学与技术学院,成都610041

出　　处：《计算机应用》2021年第2期486-491,共6页journal of Computer Applications

基　　金：国家自然科学基金青年科学基金资助项目(61802321);四川省科技厅重点研发项目(2020YFN0019)。

摘　　要：针对传统F-X域预测滤波去除地震资料随机噪声耗时巨大的问题,提出了基于统一计算设备架构(CUDA)的并行算法。首先,对算法进行模块化分析以找到算法的计算瓶颈;然后从每个窗口数据计算相关矩阵、求滤波因子、滤波等步骤入手,使用图形处理器(GPU)将滤波过程分解为多个任务并行处理;最后,对算法进行并行实现,并对相邻滤波窗口的数据冗余读取进行优化以提升算法效率。基于NVIDIA Tesla K20c显卡的实验结果表明,在250×250大小工区的地震数据中,所提并行算法较原串行算法在效率上实现了10.9倍的提升,同时能保证工程中要求的计算精度。Concerning the high time complexity problem of traditional F-X domain predictive filtering in suppressing random noise of seismic data,a parallel algorithm based on Compute Unified Device Architecture(CUDA)was proposed.Firstly,the algorithm was analyzed modularly to find the calculation bottleneck of the algorithm.Then,starting with the steps of calculating the correlation matrix,calculating the filter factor,filtering from each window data and so on,the filtering process was divided into multiple tasks for parallel processing based on the Graphic Processing Unit(GPU).Finally,the efficiency of the algorithm was improved by implementing the parallel algorithm and optimizing the redundant data reading in adjacent filter windows.Experimental results based on NVIDIA Tesla K20c show that in the seismic data of 250×250 work area,the proposed parallel algorithm achieves an efficiency improvement of 10.9 times compared with the original serial algorithm,while ensuring the calculation accuracy required in the engineering at the same time.

关 键 词：统一计算设备架构 并行计算 F-X域预测滤波 图形处理器 冗余读取优化 

分 类 号：TP338.6[自动化与计算机技术—计算机系统结构]