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001017826 037__ $$aFZJ-2023-04350
001017826 041__ $$aEnglish
001017826 1001_ $$0P:(DE-Juel1)171343$$aCavallaro, Gabriele$$b0$$eCorresponding author$$ufzj
001017826 1112_ $$aConference on Big Data from Space 2023$$cVienna$$d2023-11-06 - 2023-11-09$$gBiDS’23$$wAustria
001017826 245__ $$aChallenges and Opportunities in the Adoption of High Performance Computing for Earth Observation in the Exascale Era
001017826 260__ $$bPublications Office of the European Union$$c2023
001017826 29510 $$aProceedings of the 2023 Conference on Big Data from Space (BiDS’23) - From foresight to impact
001017826 300__ $$a25-28
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001017826 520__ $$aHigh-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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001017826 536__ $$0G:(EU-Grant)951733$$aRAISE - Research on AI- and Simulation-Based Engineering at Exascale (951733)$$c951733$$fH2020-INFRAEDI-2019-1$$x1
001017826 536__ $$0G:(EU-Grant)101033975$$aEUPEX - EUROPEAN PILOT FOR EXASCALE (101033975)$$c101033975$$fH2020-JTI-EuroHPC-2020-1$$x2
001017826 536__ $$0G:(EU-Grant)951732$$aEUROCC - National Competence Centres in the framework of EuroHPC (951732)$$c951732$$fH2020-JTI-EuroHPC-2019-2$$x3
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001017826 7001_ $$0P:(DE-Juel1)178695$$aSedona, Rocco$$b1$$ufzj
001017826 7001_ $$0P:(DE-Juel1)132239$$aRiedel, Morris$$b2$$ufzj
001017826 7001_ $$0P:(DE-Juel1)165948$$aLintermann, Andreas$$b3$$ufzj
001017826 7001_ $$0P:(DE-Juel1)138295$$aMichielsen, Kristel$$b4$$ufzj
001017826 773__ $$a10.2760/46796
001017826 8564_ $$uhttps://juser.fz-juelich.de/record/1017826/files/Cavallaro%20et%20al._2023_Challenges%20and%20Opportunities%20in%20the%20Adoption%20of%20High%20Performance%20Computing%20for%20Earth%20Observation%20in%20the%20Exascal%282%29.pdf$$yRestricted
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