Scalable Workflows for Remote Sensing Data Processing with the Deep-Est Modular Supercomputing Architecture
Ernir Erlingsson, Gabriele Cavallaro, Helmut Neukirchen, Morris Riedel · 2019
The implementation of efficient remote sensing workflows is essential to improve the access to and analysis of the vast amount of sensed data and to provide decision-makers with clear, timely, and useful information. The Dynamical Exascale Entry Platform (DEEP) is an European pre-exascale platform that incorporates heterogeneous High-Performance Computing (HPC) systems, i.e., hardware modules which include specialised accelerators. This paper demonstrates the potential of such diverse modules for the deployment of remote sensing data workflows that include diverse processing tasks. Particular focus is put on pipelines which can use the Network Attached Memory (NAM), which is a novel supercomputer module that allows near processing and/or fast shared storage of big remote sensing datasets.