TY  - CONF
AU  - Fabian, Hoppe
AU  - Gutiérrez Hermosillo Muriedas, J. P.
AU  - Tarnawa, Michael
AU  - Philipp, Knechtges
AU  - Hagemeier, Björn
AU  - Krajsek, Kai
AU  - Alexander, Rüttgers
AU  - Markus, Götz
AU  - Comito, Claudia
TI  - Engineering a large-scale data analytics and array computing library for research: Heat
PB  - Electronic Communications of the EASST
M1  - FZJ-2025-03345
SP  - 1-26
PY  - 2025
AB  - Heat is a Python library for massively-parallel and GPU-accelerated arraycomputing and machine learning. It is developed by researchers for researchers,with the ultimate goal to make multi-dimensional array processing and machinelearning for scientists (almost) as easy on a supercomputer as it is on a workstationwith NumPy or scikit-learn. This paper highlights the relevance of this project to theresearch software engineering community by giving a short, but illustrative overviewof Heat and discusses its role in the context of related libraries with a specific focuson its research software aspects.
T2  - Fourth Conference on Research Software Engineering in Germany, deRSE24
CY  - 5 Mar 2024 - 7 Mar 2024, Würzburg (Germany)
Y2  - 5 Mar 2024 - 7 Mar 2024
M2  - Würzburg, Germany
KW  - Multi-dimensional Arrays (Other)
KW  - Machine learning (Other)
KW  - Data Science (Other)
KW  - Data analytics (Other)
KW  - High-Performance Computing (Other)
KW  - Parallel Computing (Other)
KW  - GPUs (Other)
KW  - Big Data (Other)
KW  - Research Software (Other)
LB  - PUB:(DE-HGF)8 ; PUB:(DE-HGF)7
DO  - DOI:10.14279/eceasst.v83.2626
UR  - https://juser.fz-juelich.de/record/1044789
ER  -