001019998 001__ 1019998
001019998 005__ 20240105202147.0
001019998 037__ $$aFZJ-2023-05813
001019998 1001_ $$0P:(DE-HGF)0$$aHoppe, Fabian$$b0$$eCorresponding author
001019998 1112_ $$aHelmholtz AI Conference$$cHamburg$$d2023-06-12 - 2023-06-14$$wGermany
001019998 245__ $$aScaling data-intensive analytics with Heat: a Python library for massively-parallel array computing and machine learning
001019998 260__ $$c2023
001019998 3367_ $$033$$2EndNote$$aConference Paper
001019998 3367_ $$2DataCite$$aOther
001019998 3367_ $$2BibTeX$$aINPROCEEDINGS
001019998 3367_ $$2DRIVER$$aconferenceObject
001019998 3367_ $$2ORCID$$aLECTURE_SPEECH
001019998 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1704436025_29017$$xAfter Call
001019998 520__ $$aManipulating and processing massive data sets is challenging. For the vast majority of research communities, those without a background in high-performance computing, the standard approach involves breaking up and analyzing data in smaller chunks, an inefficient and very prone-to-errors process.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. Developed in collaboration by three institutions of the Helmholtz Association (KIT, FZJ, DLR), Heat:    enables memory distribution of n-dimensional arrays,    adopts PyTorch as process-local compute engine (hence supporting GPU-acceleration),    provides memory-distributed (i.e., multi-node, multi-GPU) array operations and algorithms, optimizing asynchronous MPI-communication under the hood, and    wraps functionalities in NumPy- or scikit-learn-like API to achieve porting of existing applications with minimal changes.In this presentation, we will provide an overview of the Heat library's features and capabilities and discuss its role in the ecosystem of distributed array computing and machine learning in Python. Additionally, we will highlight Heat's role as a platform for cross-discipline collaboration in data-intensive research, and address technical and operational challenges in Heat development.
001019998 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
001019998 536__ $$0G:(DE-HGF)POF4-5112$$a5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x1
001019998 536__ $$0G:(DE-Juel1)Helmholtz-SLNS$$aSLNS - SimLab Neuroscience (Helmholtz-SLNS)$$cHelmholtz-SLNS$$x2
001019998 7001_ $$0P:(DE-Juel1)174573$$aComito, Claudia$$b1
001019998 7001_ $$0P:(DE-HGF)0$$aGutiérrez Hermosillo Muriedas, Juan Pedro$$b2
001019998 7001_ $$0P:(DE-HGF)0$$aGötz, Markus$$b3
001019998 7001_ $$0P:(DE-Juel1)132123$$aHagemeier, Björn$$b4
001019998 7001_ $$0P:(DE-HGF)0$$aKnechtges, Philipp$$b5
001019998 7001_ $$0P:(DE-Juel1)129347$$aKrajsek, Kai$$b6
001019998 7001_ $$0P:(DE-HGF)0$$aRüttgers, Alexander$$b7
001019998 7001_ $$0P:(DE-HGF)0$$aStreit, Achim$$b8
001019998 7001_ $$0P:(DE-Juel1)178977$$aTarnawa, Michael$$b9
001019998 8564_ $$uhttps://helmholtzai-conference2023.de/program/
001019998 909CO $$ooai:juser.fz-juelich.de:1019998$$pVDB
001019998 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)174573$$aForschungszentrum Jülich$$b1$$kFZJ
001019998 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)132123$$aForschungszentrum Jülich$$b4$$kFZJ
001019998 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)129347$$aForschungszentrum Jülich$$b6$$kFZJ
001019998 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)178977$$aForschungszentrum Jülich$$b9$$kFZJ
001019998 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$$x0
001019998 9131_ $$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
001019998 9141_ $$y2023
001019998 920__ $$lyes
001019998 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
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