# 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 particular, a preliminary assessment of the potentials of GPU processing was carried out in [210] by proposing a GPU implementation of InSAR time-consuming algorithm kernels. More recently, a GPU based implementation of the persistent scatterer-InSAR (PS-InSAR) algorithm was proposed [211]. The solution is implemented on an architecture based on NVIDIA GPU, thus exhibiting scalable performance with GPU number and workload size. It was implemented by using the CUDA programming language. Furthermore, a GPU accelerated InSAR processing chain targeting S-1 TOPS images was developed in [212]. The new GPU-based S-1 interferometric modules are developed under the CUDA framework for NVIDIA architecture. The tests were conducted on an S-1 IW image pair and show a computational runtime reduced to 8.59 s, with a speedup of 165 compared to the serial version. However, the approach is limited to coregistration, resampling, enhanced spectral diversity (ESD), and coherence estimation only. In this  
Figure 9: P-SBAS algorithm applied to COSMO Sky-Med dataset (from [205]): (a) deformation _mean-velocity_ map relevant to the Napoli Bay area is depicted. The graph of the displacement time-series pertinent to a specific pixel located in the area of maximum deformation is also shown; (b) speedup as a function of the number of engaged processors. case, the peculiarity of the GPU processing has been, however, addressed by resorting to some specific design adoptions, as pre-calculated convolution kernels for performing interpolation stored in lookup tables to save the runtime computations. A final remark is in order. In the last decade, we have witnessed some efforts in demonstrating the ability of SAR interferometric processing over extended geographical areas (at the national or continental scale). Accordingly, several parallel solutions have been established in recent years to deal with large-scale data processing, and a selected subset of them is referred to in the following. As already mentioned, an example of InSAR data processing to obtain deformation results at the national scale (the whole Italian territory) by employing ERS, Envisat and COSMO-SkyMed data, has been presented in [213]. In [214], national-scale deformation analysis over Germany using interferometric processing of Sentinel-1 data was obtained without resorting to parallel computing. Furthermore, the P-CSInSAR approach in [206; 207; 208; 209; 215] was explicitly dedicated to the efficient generation of deformation results from medium resolution data of the C-band Sentinel-1 mission that was initially designed for large-scale interferometric applications. Overall, some of the presented approaches lack a detailed description of the adopted parallel algorithms and the associated computational performance, thus generally offering poor informative content in this regard. It should also be noted that providing ground deformation time-series over large areas does not demonstrate the effectiveness of the adopted computational strategy, which, on the contrary, should be methodologically assessed according to appropriate HPC performance metrics (Section 4). Finally, a terminological concern is in order. With specific reference to the InSAR processing arena, we highlight that recently the term \"automatic\" has been inappropriately associated with parallelization techniques, which might be misleading. It is helpful to recall that \"automatic parallelization\" (also referred to as auto parallelization) conceptually refers to the longstanding topic of compiler research regarding the compiling for parallelism [216].