Scalable performance analysis of the parallel SBAS-DInSAR algorithm

The effective exploitation of the available huge SAR data archives in reasonable time-frames has motivated the development of P-SBAS, a parallel computing solution for the SBAS (Small BAseline Subset) processing chain. Hence, P-SBAS parallel solution represents a valuable tool for the analysis of the complex phenomena characterizing the surface deformation dynamics of Earth large areas, since it permits to exploit the parallelism offered by the modern computational platforms. In this paper, the performance of the parallel algorithm P-SBAS is investigated. The quantitative evaluation of the computational efficiency of the implemented parallel prototype in terms of achieved speedup is addressed to demonstrate the effectiveness of the proposed approach. An experimental analysis has been carried out on real data by employing a computational platform comprising 32 processors. In particular, the performance analysis has been conducted by exploiting different SAR datasets pertinent to different sensors (Envisat and Cosmo Sky-Med) and the factors limiting the inherent scalability are discussed.

[1]  R. Goldstein,et al.  Mapping small elevation changes over large areas: Differential radar interferometry , 1989 .

[2]  Michele Manunta,et al.  SBAS-DInSAR Parallel Processing for Deformation Time-Series Computation , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[3]  Michele Manunta,et al.  SBAS-DInSAR time series generation on cloud computing platforms , 2013, 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS.

[4]  Hesham El-Rewini,et al.  Advanced Computer Architecture and Parallel Processing , 2005 .

[5]  Mostafa Abd-El-Barr,et al.  Advanced Computer Architecture and Parallel Processing: El-Rewini/Advanced Computer Architecture , 2004 .

[6]  P Snoeij,et al.  Sentinel-1 radar mission: Status and performance , 2009, IEEE Aerospace and Electronic Systems Magazine.

[7]  Gerhard Wellein,et al.  Introduction to High Performance Computing for Scientists and Engineers , 2010, Chapman and Hall / CRC computational science series.