TY  - CONF
AU  - Krajsek, Kai
AU  - Comito, Claudia
AU  - Götz, Markus
AU  - Hagemeier, Björn
AU  - Knechtges, Philipp
AU  - Siggel, Martin
TI  - The Helmholtz Analytics Toolkit (HeAT) - A Scientific Big Data Library for HPC -
VL  - 40
CY  - Jülich
PB  - Forschungszentrum Jülich GmbH Zentralbibliothek
M1  - FZJ-2019-06336
SN  - 978-3-95806-392-1
T2  - IAS Series
SP  - 57-60
PY  - 2019
AB  - 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.
T2  - Extreme Data Workshop 2018
CY  - 18 Sep 2018 - 19 Sep 2018, Jülich (Germany)
Y2  - 18 Sep 2018 - 19 Sep 2018
M2  - Jülich, Germany
LB  - PUB:(DE-HGF)8 ; PUB:(DE-HGF)7
UR  - https://juser.fz-juelich.de/record/867721
ER  -