A highly scalable parallel computation strategy and optimized implementation for Fresnel Seismic Tomography

Yongan Gao; Changhai Zhao; Chuang Li; Haihua Yan; Liang Zhao

doi:10.1117/12.2021194

13 March 2013 A highly scalable parallel computation strategy and optimized implementation for Fresnel Seismic Tomography

Yongan Gao, Changhai Zhao, Chuang Li , Haihua Yan, Liang Zhao

Author Affiliations +

Proceedings Volume 8783, Fifth International Conference on Machine Vision (ICMV 2012): Computer Vision, Image Analysis and Processing; 87831R (2013) https://doi.org/10.1117/12.2021194
Event: Fifth International Conference on Machine Vision (ICMV 12), 2012, Wuhan, China

Abstract

Fresnel Seismic Tomography which uses a huge amount of seismic data is an efficient methodology of researching three-dimensional structure of earth. However, in practical application, it confronts with two key challenges of enormous data volume and huge computation. It is difficult to accomplish computation tasks under normal operating environment and computation strategies. In this paper, a Job-By-Application parallel computation strategy, which uses MPI (Message Passing Interface) and Pthread hybrid programming models based on the cluster, is designed to implement Fresnel seismic tomography, this method can solve the problem of allocating tasks dynamically, improve the load balancing and scalability of the system effectively; and we adopted the cached I/O strategy to accommodate the limited memory resources. Experimental results demonstrated that the program implemented on these strategies could completed the actual job within the idea time, the running of the program was stable, achieved load balancing, showed a good speedup and could adapt to the hardware environment of insufficient memory.

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Yongan Gao, Changhai Zhao, Chuang Li , Haihua Yan, and Liang Zhao "A highly scalable parallel computation strategy and optimized implementation for Fresnel Seismic Tomography", Proc. SPIE 8783, Fifth International Conference on Machine Vision (ICMV 2012): Computer Vision, Image Analysis and Processing, 87831R (13 March 2013); https://doi.org/10.1117/12.2021194

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KEYWORDS

Tomography

Computing systems

Data modeling

Computer programming

Data processing

Data storage

Receivers

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