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001002257 0247_ $$2doi$$a10.5281/ZENODO.7637978
001002257 037__ $$aFZJ-2023-01239
001002257 041__ $$aEnglish
001002257 1001_ $$0P:(DE-Juel1)174573$$aComito, Claudia$$b0$$eCorresponding author
001002257 1112_ $$aSciOps 2022: Artificial Intelligence for Science and Operations in Astronomy$$cGarching$$d2022-05-16 - 2022-05-20$$gSciOps22$$wGermany
001002257 245__ $$aThe missing link between massive data and AI: parallel computing with Heat
001002257 260__ $$c2022
001002257 3367_ $$033$$2EndNote$$aConference Paper
001002257 3367_ $$2DataCite$$aOther
001002257 3367_ $$2BibTeX$$aINPROCEEDINGS
001002257 3367_ $$2DRIVER$$aconferenceObject
001002257 3367_ $$2ORCID$$aLECTURE_SPEECH
001002257 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1677573593_7385$$xAfter Call
001002257 520__ $$aWhen it comes to enhancing exploitation of massive data, machine learning and AI methods are very much at the forefront of our awareness. Much less so is the need for, and complexity of, applying these techniques efficiently across memory-distributed data volumes. Heat [1, 2] is an open-source Python library for high-performance data analytics, machine learning, and deep learning. It provides highly optimized algorithms and data structures for tensor computations using CPUs, GPUs and distributed cluster systems. Heat's Numpy-like API makes writing scalable, GPU-accelerated applications straightforward - at the same time, parallelism implemented under the hood via MPI provides a significant improvement in efficiency and performance with respect to, e.g., Dask. Born out of a large-scale collaboration in applied sciences, Heat also acts a platform for collaboration and knowledge transfer within data-intensive science. In this presentation, I will show you the inner workings of the library, tell you about our collaborations with the astrophysics and space science community (massively parallel signal-processing capabilities for the SKA-MPG telescope among others) and hopefully gain from you some insight into how to best support data- intensive astro operations going forward.   References: [1] Gotz, M., Debus, C., Coquelin, et al.: 'HeAT - a Distributed and GPU-accelerated Tensor Framework for Data Analytics'; [2] https://github.com/helmholtz-analytics/heat
001002257 536__ $$0G:(DE-HGF)POF4-5112$$a5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
001002257 536__ $$0G:(DE-Juel1)Helmholtz-SLNS$$aSLNS - SimLab Neuroscience (Helmholtz-SLNS)$$cHelmholtz-SLNS$$x1
001002257 588__ $$aDataset connected to DataCite
001002257 650_7 $$2Other$$amemory-distributed computing
001002257 650_7 $$2Other$$aparallel computing
001002257 650_7 $$2Other$$adata-intensive science
001002257 650_7 $$2Other$$aBig Data Analytics
001002257 650_7 $$2Other$$aPython
001002257 650_7 $$2Other$$aMessage Passing Interface
001002257 650_7 $$2Other$$aPyTorch
001002257 650_7 $$2Other$$aNumPy
001002257 650_7 $$2Other$$amachine learning
001002257 7001_ $$0P:(DE-Juel1)132123$$aHagemeier, Björn$$b1
001002257 7001_ $$0P:(DE-Juel1)178977$$aTarnawa, Michael$$b2
001002257 7001_ $$0P:(DE-Juel1)129347$$aKrajsek, Kai$$b3
001002257 773__ $$a10.5281/ZENODO.7637978
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001002257 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)174573$$aForschungszentrum Jülich$$b0$$kFZJ
001002257 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)132123$$aForschungszentrum Jülich$$b1$$kFZJ
001002257 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)178977$$aForschungszentrum Jülich$$b2$$kFZJ
001002257 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)129347$$aForschungszentrum Jülich$$b3$$kFZJ
001002257 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$$x0
001002257 9141_ $$y2022
001002257 920__ $$lyes
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