Research and Performance Analysis on Parallel Computing of Remote Sensing Image Based on MPICH

Abstract:

The traditional centralized single mode becomes a “bottleneck” of remote sensing image processing which cannot meet the needs of future remote sensing image processing development. Fortunately, the distributed parallel computing has provided a turning point to the quick calculation of remote sensing image. This paper presents the cluster computing environment based on the MPI, and advances a project of a parallelized design to the gray level co-occurrence matrix algorithm. Moreover, the experimental data, which is due to the parallelized algorithm running in the cluster, is recorded and analyzed in several respects such as different nodes, time, speedup, efficiency and so on. The analyzed result shows that parallel computing cluster based on MPICH can efficiently improve the speed of remote sensing image processing in the case of more complex algorithms. However, when the number of node increases, the consuming time decreases, and the efficiency will decrease at the same time. So, it is rather important to keep the balance between performance and efficiency. The nodes can not be continuously added into computing, when the consuming time can be accepted.

• Periodical: Advanced Materials Research (Volume 680)
• Pages: 540-545
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.680.540
• Online since: April 2013
• Authors: Jun Li, Wei Feng Ma
• Keywords: MPICH, Parallel Computing, Remote Sensing Image Processing

References

[1] MA Weifeng, CEN Gang, LI Jun, SHEN Zhanfeng. Analysis on High-powered Remote Sensing Image Processing and Spatial Information Grid Modeling[J]. Computer Engineering. 2006. 03/Vol32. No. 5.

[2] Weifeng MA, Jun LI, Zhiyi XU, ect. Research on Distributed Geocomputation Environment Adapting to Remotely Sensed Image Processing. Proceedings of ICCSE 2007, pp.666-670.

[3] How to run an Example Pragram Using MPI, http: /www. chpcc. edu. cn/scalapck-ug/node26. html.

[4] Barry Wilkinson, Michael Allen, Parallel Programming—Techniques and Applications Using Networked Workstation and Parallel Computers, Prentice Hall (2002).

[5] Du Zhihui, ed, high-performance computing parallel programming - MPI parallel programming, Tsinghua University Press, 2001. 8.