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001007353 0247_ $$2doi$$a10.23919/MIPRO55190.2022.9803802
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001007353 037__ $$aFZJ-2023-02022
001007353 041__ $$aEnglish
001007353 1001_ $$0P:(DE-Juel1)132239$$aRiedel, M.$$b0$$eCorresponding author$$ufzj
001007353 1112_ $$a45th Jubilee International Convention on Information, Communication and Electronic Technology (MIPRO)$$cOpatija$$d2022-05-23 - 2022-05-27$$gMIPRO$$wCroatia
001007353 245__ $$aPractice and Experience using High Performance Computing and Quantum Computing to Speed-up Data Science Methods in Scientific Applications
001007353 260__ $$bIEEE$$c2022
001007353 300__ $$a281-286
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001007353 520__ $$aHigh-Performance Computing (HPC) can quickly process scientific data and perform complex calculations at extremely high speeds. A vast increase in HPC use across scientific communities is observed, especially in using parallel data science methods to speed-up scientific applications. HPC enables scaling up machine and deep learning algorithms that inherently solve optimization problems. More recently, the field of quantum machine learning evolved as another HPC related approach to speed-up data science methods. This paper will address primarily traditional HPC and partly the new quantum machine learning aspects, whereby the latter specifically focus on our experiences on using quantum annealing at the Juelich Supercomputing Centre (JSC). Quantum annealing is particularly effective for solving optimization problems like those that are inherent in machine learning methods. We contrast these new experiences with our lessons learned of using many parallel data science methods with a high number of Graphical Processing Units (GPUs). That includes modular supercomputers such as JUWELS, the fastest European supercomputer at the time of writing. Apart from practice and experience with HPC co-design applications, technical challenges and solutions are discussed, such as using interactive access via JupyterLab on typical batch-oriented HPC systems or enabling distributed training tools for deep learning on our HPC systems.
001007353 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
001007353 536__ $$0G:(EU-Grant)951733$$aRAISE - Research on AI- and Simulation-Based Engineering at Exascale (951733)$$c951733$$fH2020-INFRAEDI-2019-1$$x1
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001007353 7001_ $$0P:(DE-HGF)0$$aBook, M.$$b1
001007353 7001_ $$0P:(DE-HGF)0$$aNeukirchen, H.$$b2
001007353 7001_ $$0P:(DE-Juel1)171343$$aCavallaro, G.$$b3$$ufzj
001007353 7001_ $$0P:(DE-Juel1)165948$$aLintermann, A.$$b4$$ufzj
001007353 773__ $$a10.23919/MIPRO55190.2022.9803802
001007353 8564_ $$uhttps://ieeexplore.ieee.org/document/9803802
001007353 8564_ $$uhttps://juser.fz-juelich.de/record/1007353/files/MIPRO2022__RAISE_Paper.pdf$$yOpenAccess
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001007353 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)171343$$aForschungszentrum Jülich$$b3$$kFZJ
001007353 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)165948$$aForschungszentrum Jülich$$b4$$kFZJ
001007353 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5111$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x0
001007353 9141_ $$y2023
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001007353 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
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