# High Performance Computing in Satellite SAR Interferometry: A Critical Perspective
## 6 Selected MT-InSAR Techniques Using HPC or Cloud-Based Platforms
### MT-InSAR Processing Using HPC  
In this section, selected HPC approaches involving the entire MT-InSAR processing chain to obtain ground deformation products are addressed. A parallel MPI-based scheme implementing InSAR processing was initially proposed in 2005 [202], according to a parallelization strategy in which the input data is suitably decomposed in partially overlapping patches. In [203], the authors presented results of the processing, through advanced persistent scatterer interferometry (PSI) techniques, about 20,000 SAR (ERS, Envisat, and COSMO-SkyMed) images acquired from 1992 to 2014 over the whole Italian territory using high-performance computing (HPC) on cluster systems; however, no details on the implemented parallel algorithms and corresponding performance were provided. A parallel computing strategy implementing the _parallel small baseline subset_ (P-SBAS) technique for time-series analysis of the ground deformation was originally proposed in [204], and the inherently scalable performance analysis for a cluster environment was \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Processing Stage** & **Selected Works** & **Parallelism** & **Tools** & **Year** \\ \hline \multirow{8}{*}{Focusing} & [181, 182] & Multithreading & OpenMP & 2016 \\ \cline{2-5}  & [183, 184] & GPU-based & OpenCL & 2019 \\ \cline{2-5}  & [185] & GPU-based & CUDA & 2020 \\ \cline{2-5}  & [188] & GPU-based & CUDA & 2014 \\ \cline{2-5}  & [186] & GPUs + CPUs & CUDA/OpenMP & 2016 \\ \cline{2-5}  & [189] & multi-GPU & CUDA/MPI & 2018 \\ \cline{2-5}  & [187] & GPU-based & CUDA & 2016 \\ \hline \multirow{4}{*}{Coregistration} & [190] & Multiprocessing & MPI & 2002 \\ \cline{2-5}  & [49] & Multithreading & OpenMP & 2021 \\ \cline{2-5}  & [191] & GPU-based & CUDA & 2014 \\ \cline{2-5}  & [192] & GPU-based & CUDA & 2020 \\ \hline \multirow{4}{*}{Phase Unwrapping} & [198] & Dual-level & MPI/OpenMP & 2015 \\ \cline{2-5}  & [199] & Multithreading & OpenMP & 2015 \\ \cline{2-5}  & [200] & GPU-based & CUDA & 2014 \\ \cline{2-5}  & [201] & GPU-based & CUDA & 2017 \\ \hline \multirow{3}{*}{InSAR Filtering} & [190] & Multiprocessing & MPI & 2015 \\ \cline{2-5}  & [194] & GPU-based & CUDA & 2016 \\ \cline{1-1} \cline{2-5}  & [195] & GPU-based & OpenCL & 2020 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the reviewed InSAR Fundamental Stages using HPC. discussed in [205]. As an example, ground deformation results relevant to the Napoli Bay area (Italy), and parallel performance achieved with the P-SBAS processing technique applied to a COSMO Sky-Med dataset, are depicted in Figure 9a,b, respectively. It is evident that residual sequential parts of the algorithm limit the scalable performance (see Section 4.3). Parallel coherent scatterers InSAR (P-CSInSAR) approaches for processing Sentinel-1 data are proposed in [206; 207; 208], where several parallelism strategies are applied for each processing step according to different levels of granularity (burst-level, line-level, and block-level) [208]. The adopted strategy includes both intra-node (multithreading) and inter-node (message-passing) parallelisms. According to [207], the InSAR measurement at the nationwide level has been carried out using Sentinel-1 data (more than 700 TB) acquired over the whole China territory (from September 2018 to December 2019). However, the parallel performance achieved shows that the adopted parallel strategy results in limited scalable performances [206]. The Open Source SAR Investigation System (OSARIS) is another framework to process large stacks of S1 data on high performance computing clusters. It is based on the well-known GMT-SAR tool [209] jointly with shell scripts. It provides a framework where parallelization, flexibility, and complete analysis can be used to implement a monitoring service [209]. However, the computational relevance is limited since parallel algorithms and associated performance have not been detailed. Other approaches, focused mainly on GPU exploitation, have also been proposed.