Pracniques: further remarks on reducing truncation errors
Communications of the ACM
Brook for GPUs: stream computing on graphics hardware
ACM SIGGRAPH 2004 Papers
Accelerator: using data parallelism to program GPUs for general-purpose uses
Proceedings of the 12th international conference on Architectural support for programming languages and operating systems
Parallel processing of Prestack Kirchhoff Time Migration on a PC Cluster
Computers & Geosciences
Accelerating Kirchhoff Migration by CPU and GPU Cooperation
SBAC-PAD '09 Proceedings of the 2009 21st International Symposium on Computer Architecture and High Performance Computing
3D magnetic inversion based on probability tomography and its GPU implement
Computers & Geosciences
Parallel design for error-resilient entropy coding algorithm on GPU
Journal of Parallel and Distributed Computing
3D seismic reverse time migration on GPGPU
Computers & Geosciences
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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.