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001026335 005__ 20240715202025.0
001026335 0247_ $$2datacite_doi$$a10.34734/FZJ-2024-03386
001026335 037__ $$aFZJ-2024-03386
001026335 041__ $$aEnglish
001026335 1001_ $$0P:(DE-Juel1)185995$$aBaker, Dirk$$b0$$eCorresponding author$$ufzj
001026335 1112_ $$anordic e-Infrastructure Collaboration Conference$$cTallinn$$d2024-05-27 - 2024-05-29$$gNeIC2024$$wEstonia
001026335 245__ $$aAdapting Agricultural Virtual Environments in Game Engines to Improve HPC Accessibility
001026335 260__ $$bSpringer$$c2024
001026335 29510 $$aCommunications in Computer and Information Science
001026335 300__ $$a1-15
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001026335 520__ $$aE-infrastructures deliver basic supercomputing and storage capabilities but can benefit from innovative higher-level services that enable use-cases in critical domains, such as environmental and agricultural science.This work describes methods to distribute virtual scenes to the GPU nodes of a modular supercomputer for data generation.High information density virtual scenes, containing >100k geometries, typically cannot be rendered in real-time without techniques that change the information content, such as level-of-detail or culling approaches.Our work enables the concurrent and partitioned coupling to the image analysis in such a way that the data generation is dynamic and can be allocated to GPU nodes on demand, resulting in the possibility of moving through a continuous virtual scene rendered on multiple nodes.Within agricultural data analysis, the approach is especially impactful as virtual fields contain many individual geometries that coexist in one continuous system.Our work facilitates the generation of high-quality image data sets which has the potential to solve the challenge of scarcity of well-annotated data in agricultural science.We use real-time communication standards to couple the data production with the image analysis training.We demonstrate how the use-case rendering impacts effective use of the compute nodes and furthermore develop techniques to distribute the workload to improve the data production.
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001026335 536__ $$0G:(DE-HGF)POF4-2173$$a2173 - Agro-biogeosystems: controls, feedbacks and impact (POF4-217)$$cPOF4-217$$fPOF IV$$x2
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001026335 536__ $$0G:(GEPRIS)390732324$$aDFG project 390732324 - EXC 2070: PhenoRob - Robotik und Phänotypisierung für Nachhaltige Nutzpflanzenproduktion (390732324)$$c390732324$$x4
001026335 536__ $$0G:(DE-HGF)POF4-5112$$a5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x5
001026335 7001_ $$0P:(DE-Juel1)186730$$aBauer, Felix$$b1$$ufzj
001026335 7001_ $$0P:(DE-Juel1)157922$$aSchnepf, Andrea$$b2$$ufzj
001026335 7001_ $$0P:(DE-Juel1)129394$$aScharr, Hanno$$b3$$ufzj
001026335 7001_ $$0P:(DE-Juel1)132239$$aRiedel, Morris$$b4$$ufzj
001026335 7001_ $$0P:(DE-Juel1)168541$$aGöbbert, Jens Henrik$$b5$$ufzj
001026335 7001_ $$0P:(DE-HGF)0$$aHvannberg, Ebba$$b6
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001026335 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)185995$$a University of Iceland$$b0
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001026335 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a University of Iceland$$b6
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001026335 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5112$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x3
001026335 9141_ $$y2024
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