001034687 001__ 1034687
001034687 005__ 20250113214946.0
001034687 037__ $$aFZJ-2024-07444
001034687 041__ $$aEnglish
001034687 1001_ $$0P:(DE-Juel1)174573$$aComito, C.$$b0$$ufzj
001034687 1112_ $$aHelmholtz AI Conference$$cDüsseldorf$$d2024-06-12 - 2024-06-14$$wGermany
001034687 245__ $$aScaling data-intensive analytics with Heat: a Python library for massively-parallel array computing and machine learning
001034687 260__ $$c2024
001034687 3367_ $$033$$2EndNote$$aConference Paper
001034687 3367_ $$2BibTeX$$aINPROCEEDINGS
001034687 3367_ $$2DRIVER$$aconferenceObject
001034687 3367_ $$2ORCID$$aCONFERENCE_POSTER
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001034687 520__ $$aHandling and analyzing massive data sets is highly important for the vast majority of research communities, but it is also challenging, especially for those communities without a background in HPC. The Helmholtz Analytics Toolkit (Heat) library offers a solution to this problem by providing memory-distributed and hardware-accelerated array manipulation, data analytics, and machine learning algorithms in Python, targeting the usage by non-experts in HPC. In short: Heats objective is to make array computing and machine learning as easy on a CPU/GPU-cluster as it is on a workstation.Our poster provides an overview of Heats design principles, its current features and capabilities, and discusses its role in the ecosystem of distributed array computing and machine learning in Python.
001034687 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
001034687 536__ $$0G:(DE-HGF)POF4-5112$$a5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x1
001034687 536__ $$0G:(DE-Juel1)Helmholtz-SLNS$$aSLNS - SimLab Neuroscience (Helmholtz-SLNS)$$cHelmholtz-SLNS$$x2
001034687 7001_ $$0P:(DE-HGF)0$$aGutiérrez Hermosillo Muriedas, J. P.$$b1
001034687 7001_ $$0P:(DE-Juel1)162390$$aGötz, M.$$b2
001034687 7001_ $$0P:(DE-Juel1)132123$$aHagemeier, B.$$b3$$ufzj
001034687 7001_ $$0P:(DE-HGF)0$$aHoppe, F.$$b4$$eCorresponding author
001034687 7001_ $$0P:(DE-HGF)0$$aKnechtges, P.$$b5
001034687 7001_ $$0P:(DE-Juel1)129347$$aKrajsek, K.$$b6$$ufzj
001034687 7001_ $$0P:(DE-HGF)0$$aRüttgers, Alexander$$b7
001034687 7001_ $$0P:(DE-Juel1)178977$$aTarnawa, M.$$b8$$ufzj
001034687 909CO $$ooai:juser.fz-juelich.de:1034687$$pVDB
001034687 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)174573$$aForschungszentrum Jülich$$b0$$kFZJ
001034687 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)132123$$aForschungszentrum Jülich$$b3$$kFZJ
001034687 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)129347$$aForschungszentrum Jülich$$b6$$kFZJ
001034687 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)178977$$aForschungszentrum Jülich$$b8$$kFZJ
001034687 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$$x0
001034687 9131_ $$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$$vEnabling Computational- & Data-Intensive Science and Engineering$$x1
001034687 9141_ $$y2024
001034687 920__ $$lyes
001034687 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
001034687 980__ $$aposter
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001034687 980__ $$aI:(DE-Juel1)JSC-20090406
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