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001     867721
005     20210130003843.0
020 _ _ |a 978-3-95806-392-1
024 7 _ |a 2128/23601
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037 _ _ |a FZJ-2019-06336
041 _ _ |a English
100 1 _ |a Krajsek, Kai
|0 P:(DE-Juel1)129347
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|e Corresponding author
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111 2 _ |a Extreme Data Workshop 2018
|c Jülich
|d 2018-09-18 - 2018-09-19
|w Germany
245 _ _ |a The Helmholtz Analytics Toolkit (HeAT) - A Scientific Big Data Library for HPC -
260 _ _ |a Jülich
|c 2019
|b Forschungszentrum Jülich GmbH Zentralbibliothek
295 1 0 |a Proceedings
300 _ _ |a 57-60
336 7 _ |a CONFERENCE_PAPER
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336 7 _ |a Conference Paper
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336 7 _ |a INPROCEEDINGS
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336 7 _ |a Contribution to a book
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490 0 _ |a IAS Series
|v 40
520 _ _ |a We 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.
536 _ _ |a 511 - Computational Science and Mathematical Methods (POF3-511)
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536 _ _ |a 512 - Data-Intensive Science and Federated Computing (POF3-512)
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536 _ _ |a HAF - Helmholtz Analytics Framework (ZT-I-0003)
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536 _ _ |a SLNS - SimLab Neuroscience (Helmholtz-SLNS)
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700 1 _ |a Comito, Claudia
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700 1 _ |a Götz, Markus
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700 1 _ |a Hagemeier, Björn
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700 1 _ |a Knechtges, Philipp
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700 1 _ |a Siggel, Martin
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856 4 _ |u https://juser.fz-juelich.de/record/867721/files/krajsek_2018_extreme_data_ws_submittedversion.pdf
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914 1 _ |y 2019
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