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A Low Overhead Heterogeneous Parallel Optimization Method Based on 3-D Elastic Wave Numerical Simulation

delete2024-01-01
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PRE
AI
Z
Zhitao Zhang
Z
Zhuwen Wang
Y
Yuhang Guo *
王文强 封面图
王文强 (Wenqiang Wang)
Z
Zhaoqi Sun
W
Wubing Wan
甘
甘霖 (Lin Gan)
韩锐羿 封面图
韩锐羿 (Ruiyi Han)
王一博 封面图
王一博 (Yibo Wang)
DOI:10.1109/TGRS.2024.3396814delete
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摘要

摘要

En 中文
Applying the staggered grid finite difference method (SGFDM) for simulating acoustic responses in large-scale, complex, 3-D models poses substantial challenges in geophysics, especially in high-resolution stratigraphic model, due to the high computational burden. To address this problem, we have proposed a low overhead parallel optimization method (LOPOM) suitable for heterogeneous architectures, using high-resolution borehole models as examples. The LOPOM enhances computational intensity and optimizes memory bandwidth utilization. This is achieved through the technique of data reuse along the discontinuities of the model and by minimizing such discontinuities within the halo region. LOPOM was implemented and tested on the CUDA platform and the Sunway supercomputer. In each instance, LOPOM demonstrated an optimal acceleration ratio, thus proving its robust performance. Furthermore, the method's effectiveness was confirmed through its application to fracture-vuggy formation models and digital core models. Finally, the numerical simulation results and the actual logging data are combined to illustrate the application value of LOPOM.
Keyword:
Computational modeling
Stress
Numerical models
Solid modeling
Three-dimensional displays
Optimization methods
Load modeling
Fracture-vuggy formation
high performance computing
numerical modeling
parallel optimization method

期刊

IEEE Transactions on Geoscience and Remote Sensing 封面图
IEEE Transactions on Geoscience and Remote Sensing
IF:
8.6
论文数:
2.2W
被引数:
10.7W

机构

T
tsinghua university
学者数:
11.9W
论文数: 10.0W
被引数: 137
Z
Zhengzhou University
学者数:
6.8W
论文数: 4.4W
被引数: 8.5W
C
Chinese University of Hong Kong
学者数:
3.4W
论文数: 3.2W
被引数: 5.6W
J
Jilin University
学者数:
8.7W
论文数: 5.6W
被引数: 8.9K
C
chinese academy of sciences
学者数:
56.7W
论文数: 45.0W
被引数: 704
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