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| Hauptseite > Publikationsdatenbank > Challenges and Opportunities in the Adoption of High Performance Computing for Earth Observation in the Exascale Era > print |
| Hauptseite > Publikationsdatenbank > Challenges and Opportunities in the Adoption of High Performance Computing for Earth Observation in the Exascale Era > print |
| 001 | 1017826 | ||
| 005 | 20260122214022.0 | ||
| 024 | 7 | _ | |2 doi |a 10.2760/46796 |
| 037 | _ | _ | |a FZJ-2023-04350 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |0 P:(DE-Juel1)171343 |a Cavallaro, Gabriele |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a Conference on Big Data from Space 2023 |c Vienna |d 2023-11-06 - 2023-11-09 |g BiDS’23 |w Austria |
| 245 | _ | _ | |a Challenges and Opportunities in the Adoption of High Performance Computing for Earth Observation in the Exascale Era |
| 260 | _ | _ | |b Publications Office of the European Union |c 2023 |
| 295 | 1 | 0 | |a Proceedings of the 2023 Conference on Big Data from Space (BiDS’23) - From foresight to impact |
| 300 | _ | _ | |a 25-28 |
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| 520 | _ | _ | |a High-Performance Computing (HPC) enables precise analysis of large and complex Earth Observation (EO) datasets. However, the adoption of supercomputing in the EO community faces challenges from the increasing heterogeneity of HPC systems, limited expertise, and the need to leverage novel computing technologies. This paper explores the implications of exascale computing advancements and the inherent heterogeneity of HPC architectures. It highlights EU-supported projects optimizing software development and harnessing the capabilities of heterogeneous HPC configurations. Methodologies addressing challenges of modular supercomputing, large-scale Deep Learning (DL) models, and hybrid quantum-classical algorithms are presented, aiming to enhance the utilization of supercomputing in EO for improved research, industrial applications, and SME support. |
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| 773 | _ | _ | |a 10.2760/46796 |
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