.png){kind=icon}
.png){kind=icon}
Return
Computing prestack Kirchhoff time migration on general purpose GPU
.png){kind=icon}
PRE
AI
DOI：10.1016/j.cageo.2010.10.014.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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 CPU kernel execution time, etc. The floating-point errors on CUDA and NVidia's CPUs 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 CPU 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. (C) 2010 Elsevier Ltd. All rights reserved.
Keywords:
Kirchhoff migration
General purpose GPU
CUDA
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
C
IF:
4.4
Papers:
5.0K
Citations:
1.5W