Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (2): 486-491.DOI: 10.11772/j.issn.1001-9081.2020050688

Special Issue: 先进计算

• Advanced computing • Previous Articles     Next Articles

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

YANG Xianfeng1, GUI Hongjun1, FU Chunchang2   

  1. 1. School of Computer Science, Southwest Petroleum University, Chengdu Sichuan 610500, China;
    2. School of Computer Science and Technology, Southwest Minzu University, Chengdu Sichuan 610041, China
  • Received:2020-05-22 Revised:2020-08-26 Online:2021-02-10 Published:2020-09-15
  • Supported by:
    This work is partially supported by the Youth Program of the National Natural Science Foundation of China (61802321), the Key Research and Development Program of Science and Technology Department of Sichuan Province (2020YFN0019).


杨先凤1, 贵红军1, 傅春常2   

  1. 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
  • 通讯作者: 贵红军
  • 作者简介:杨先凤(1974-),女,四川南部人,教授,硕士,主要研究方向:计算机图像处理、数据库系统;贵红军(1995-),男,四川绵阳人,硕士研究生,主要研究方向:信号处理、并行计算;傅春常(1973-),女,四川射洪人,讲师,硕士,主要研究方向:数据库、数据挖掘。
  • 基金资助:

Abstract: 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.

Key words: Compute Unified Device Architecture (CUDA), parallel computing, F-X domain prediction filtering, Graphic Processing Unit (GPU), redundant reading optimization

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。

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

CLC Number: