4.7 Article

An optimized parallelized SGFD modeling scheme for 3D seismic wave propagation

期刊

COMPUTERS & GEOSCIENCES
卷 131, 期 -, 页码 102-111

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cageo.2019.06.017

关键词

-

资金

  1. National Natural Science Foundation of China [41630314, 41874130]
  2. National Science and Technology Program [2016ZX05010001]
  3. Major Project of the China National Petroleum Corporation

向作者/读者索取更多资源

Large-scale three-dimensional (3D) seismic modeling is considered the foundation of imaging and inversion. Parallelization strategies are crucial to solving such a computationally intensive problem. In this paper, we focus on two factors, decomposition direction and decomposition dimension, that significantly affect the computational performance. The decomposition direction determines the cache hit ratio during register addressing, and the decomposition dimension influences the communication size. We thoroughly analyze these two factors by performing time-space domain staggered-grid finite-difference (SGFD) modeling with a set of decomposition strategies. Four metrics, including computation time, speedup ratio, strong scaling property, and memory usage, are introduced to evaluate the computational performance of each trial. After theoretical analysis and metrics testing, we conclude that the optimized domain decomposition strategy is: decomposing models at two dimensions, the decomposition directions are perpendicular to the fastest and the second fastest dimensions, here we refer the dimension in which data are continuously saved as the fastest dimension. Three examples further verify the feasibility and efficiency of the optimized parallel scheme. Considering that domain decomposition-based 3D seismic parallel simulation packages are seldom available in the public domain, we provide a program template for the optimized domain decomposition strategies as an open-source package.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据