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000867721 041__ $$aEnglish
000867721 1001_ $$0P:(DE-Juel1)129347$$aKrajsek, Kai$$b0$$eCorresponding author$$ufzj
000867721 1112_ $$aExtreme Data Workshop 2018$$cJülich$$d2018-09-18 - 2018-09-19$$wGermany
000867721 245__ $$aThe Helmholtz Analytics Toolkit (HeAT) - A Scientific Big Data Library for HPC -
000867721 260__ $$aJülich$$bForschungszentrum Jülich GmbH Zentralbibliothek$$c2019
000867721 29510 $$aProceedings
000867721 300__ $$a57-60
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000867721 4900_ $$aIAS Series$$v40
000867721 520__ $$aWe present HeAT, a scientific big data librarysupporting transparent computation on HPC systems. HeATbuilds on top of PyTorch, which already provides many requiredfeatures like automatic differentiation, CPU and GPU support,linear algebra operations and basic MPI functionality as well asan imperative programming paradigm allowing fast prototypingessential in scientific research. These features are generalized toa distributed tensor with a NumPy-like interface allowing to port existing NumPy algorithms to HPC systems nearly effortlessly.
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000867721 536__ $$0G:(DE-Juel1)Helmholtz-SLNS$$aSLNS - SimLab Neuroscience (Helmholtz-SLNS)$$cHelmholtz-SLNS$$x3
000867721 7001_ $$0P:(DE-Juel1)174573$$aComito, Claudia$$b1$$ufzj
000867721 7001_ $$0P:(DE-HGF)0$$aGötz, Markus$$b2
000867721 7001_ $$0P:(DE-Juel1)132123$$aHagemeier, Björn$$b3$$ufzj
000867721 7001_ $$0P:(DE-HGF)0$$aKnechtges, Philipp$$b4
000867721 7001_ $$0P:(DE-HGF)0$$aSiggel, Martin$$b5
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