# High Performance Computing in Satellite SAR Interferometry: A Critical Perspective
## 1 Introduction  
High performance computing (HPC) is concerned with computing systems that can solve extremely complex and demanding problems in physics and engineering. HPC has been used to model difficult problems in many scientific fields, ranging from high-energy physics to medical science, from climate science to biochemical molecule design. In particular, HPC is crucial for scientific and engineering applications requesting advanced computations. Accordingly, computational science and engineering advance the frontiers of knowledge in nearly every scientific discipline, thus constituting a strategic resource for advances and innovation. HPC has evolved in the last decades with the advancement of computer hardware, network and software technology, including the maturation of cluster computing, the advent of multicore, and the rise of the graphic processing unity (GPU) for general-purpose parallel computing. A measure of the computing performance in solving problems is provided in FLOPS (floating-point operations per second), and nowadays, the fastest supercomputers operate at the _petascale_ (\(1\times 10^{15}\) FLOPS). Current supercomputing capacity is reported in detail in the TOP500 list of supercomputers [1]. Large-scale remote sensing data processing typically demands massive and sophisticated computations on a large amount of data, thus comprising computational-intensive and data-intensive applications. Specifically, the execution of such sequential processing algorithms is rather time-consuming, and accordingly, the available computational resources are typically not utilized efficiently. On the other hand, the effective exploitation of different levels of parallelism supported by HPC platforms (distributed-memory, shared-memory, multi/many-core, specialized hardware accelerators, etc.) requires a computing-domain-oriented approach and a specific knowledge of parallel processing methodologies [2]. Moreover, high performance and large-scale computing are needed for big data analysis and artificial intelligence (AI) based processing (e.g., deep learning). In this paper, we specifically focus on synthetic aperture radar interferometry (InSAR). InSAR is a widely used technique that combines SAR complex images to form interferograms, which can be used to reconstruct the topography of the Earth's surface (DEM generation) and the detection of ground displacements over large areas [3]. Earth constantly changes over a wide range of temporal and spatial scales due to natural processes and direct human activities, as volcanic eruptions, earthquakes, land subsidence, and buildings/public infrastructures settlement in urban areas. Therefore, InSAR techniques provide a unique way to resolve spatial and temporal characteristics of the Earth's surface deformation, with application to a plethora of natural and anthropogenic processes [4]. Extensive reviews on the subject are provided in [5; 6]. In the last decades, refined interferometric SAR (InSAR) processing methodologies have been developed, thereby providing a wealth of information of interest for a broader scientific community. As a matter of fact, large-scale SAR interferometric processing is recognized as being computationally intensive and data-intensive. At the same time, Earth observation (EO) platforms with enhanced SAR sensors have rapidly evolved, thus acquiring information in different portions of the microwave spectrum, with enhanced resolution and improved spatial and temporal (short revisit times) coverage. Accordingly, the image sizes are typically of the order of gigabytes; thereby, the associate data volumes of image stacks to be processed naturally leads to remarkable computational demands. It is then clear that HPC offers a powerful approach for efficiently and effectively performing these calculations, and its adoption in SAR interferometric processing is currently obtaining significant attention. In the last decades, we have witnessed an increasing interest in applying parallel computing methods to remote sensing.