Computing prestack Kirchhoff time migration on general purpose GPU

This paper introduces how to optimize a practical prestack Kirchhoff time migration program by the Compute Unified Device Architecture (CUDA) on a general purpose GPU (GPGPU). A few useful optimization methods on GPGPU are demonstrated, such as how to increase the kernel thread numbers on GPU cores,...

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Bibliographic Details
Published in:Computers & geosciences 2011-10, Vol.37 (10), p.1702-1710
Main Authors: Shi, Xiaohua, Li, Chuang, Wang, Shihu, Wang, Xu
Format: Article
Language:English
Subjects:
Applied geophysics
Central processing units
computers
CUDA
data collection
Earth sciences
Earth, ocean, space
Errors
Exact sciences and technology
Floating point arithmetic
General purpose GPU
Internal geophysics
Kernels
Kirchhoff migration
Migration
Optimization
Seismic phenomena
system optimization
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Summary:This paper introduces how to optimize a practical prestack Kirchhoff time migration program by the Compute Unified Device Architecture (CUDA) on a general purpose GPU (GPGPU). A few useful optimization methods on GPGPU are demonstrated, such as how to increase the kernel thread numbers on GPU cores, and how to utilize the memory streams to overlap GPU kernel execution time, etc. The floating-point errors on CUDA and NVidia's GPUs are discussed in detail. Some effective methods that can be used to reduce the floating-point errors are introduced. The images generated by the practical prestack Kirchhoff time migration programs for the same real-world seismic data inputs on CPU and GPU are demonstrated. The final GPGPU approach on NVidia GTX 260 is more than 17 times faster than its original CPU version on Intel's P4 3.0G.
ISSN:0098-3004
1873-7803
DOI:10.1016/j.cageo.2010.10.014