An efficient implementation of 3D high-resolution imaging for large-scale seismic data with GPU/CPU heterogeneous parallel computing

doi:10.1016/j.cageo.2017.11.020

	An efficient implementation of 3D high-resolution imaging for large-scale seismic data with GPU/CPU heterogeneous parallel computing
	Xu, Jincheng 1; Liu, Wei 1; Wang, Jin 1; Liu, Linong 1; Zhang, Jianfeng 1,2
刊名	COMPUTERS & GEOSCIENCES
	2018-02-01
卷号	111 页码:272-282
ISSN号	0098-3004
DOI	10.1016/j.cageo.2017.11.020
文献子类	Article
英文摘要	De-absorption pre-stack time migration (QPSTM) compensates for the absorption and dispersion of seismic waves by introducing an effective Q parameter, thereby making it an effective tool for 3D, high-resolution imaging of seismic data. Although the optimal aperture obtained via stationary-phase migration reduces the computational cost of 3D QPSTM and yields 3D stationary-phase QPSTM, the associated computational efficiency is still the main problem in the processing of 3D, high-resolution images for real large-scale seismic data. In the current paper, we proposed a division method for large-scale, 3D seismic data to optimize the performance of stationary-phase QPSTM on clusters of graphics processing units (GPU). Then, we designed an imaging point parallel strategy to achieve an optimal parallel computing performance. Afterward, we adopted an asynchronous double buffering scheme for multi-stream to perform the GPU/CPU parallel computing. Moreover, several key optimization strategies of computation and storage based on the compute unified device architecture (CUDA) were adopted to accelerate the 3D stationary-phase QPSTM algorithm. Compared with the initial GPU code, the implementation of the key optimization steps, including thread optimization, shared memory optimization, register optimization and special function units (SFU), greatly improved the efficiency. A numerical example employing real large-scale, 3D seismic data showed that our scheme is nearly 80 times faster than the CPU-QPSTM algorithm. Our GPU/CPU heterogeneous parallel computing framework significant reduces the computational cost and facilitates 3D high-resolution imaging for large-scale seismic data.
WOS关键词	PRESTACK DEPTH MIGRATION ; REVERSE-TIME MIGRATION ; GRAPHICS PROCESSORS ; WAVE ; EXTRAPOLATION
WOS研究方向	Computer Science ; Geology
语种	英语
出版者	PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号	WOS:000423005900026
资助机构	National Natural Science Fund of China(41330316) ; National Natural Science Fund of China(41330316) ; National Major Project of China(2017ZX05008-007) ; National Major Project of China(2017ZX05008-007) ; National Natural Science Fund of China(41330316) ; National Natural Science Fund of China(41330316) ; National Major Project of China(2017ZX05008-007) ; National Major Project of China(2017ZX05008-007) ; National Natural Science Fund of China(41330316) ; National Natural Science Fund of China(41330316) ; National Major Project of China(2017ZX05008-007) ; National Major Project of China(2017ZX05008-007) ; National Natural Science Fund of China(41330316) ; National Natural Science Fund of China(41330316) ; National Major Project of China(2017ZX05008-007) ; National Major Project of China(2017ZX05008-007)
内容类型	期刊论文
源URL	[http://ir.iggcas.ac.cn/handle/132A11/82501]
专题	中国科学院地质与地球物理研究所
通讯作者	Xu, Jincheng
作者单位	1.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Petr Resources Res, Beijing 100029, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Xu, Jincheng,Liu, Wei,Wang, Jin,et al. An efficient implementation of 3D high-resolution imaging for large-scale seismic data with GPU/CPU heterogeneous parallel computing[J]. COMPUTERS & GEOSCIENCES,2018,111:272-282.
APA	Xu, Jincheng,Liu, Wei,Wang, Jin,Liu, Linong,&Zhang, Jianfeng.(2018).An efficient implementation of 3D high-resolution imaging for large-scale seismic data with GPU/CPU heterogeneous parallel computing.COMPUTERS & GEOSCIENCES,111,272-282.
MLA	Xu, Jincheng,et al."An efficient implementation of 3D high-resolution imaging for large-scale seismic data with GPU/CPU heterogeneous parallel computing".COMPUTERS & GEOSCIENCES 111(2018):272-282.