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| Home > Publications database > Engineering a large-scale data analytics and array computing library for research: Heat > print |
| 001 | 1044789 | ||
| 005 | 20251211202153.0 | ||
| 024 | 7 | _ | |a 10.14279/ECEASST.V83.2626 |2 doi |
| 024 | 7 | _ | |a 10.14279/eceasst.v83.2626 |2 doi |
| 024 | 7 | _ | |a 10.34734/FZJ-2025-03345 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2025-03345 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Fabian, Hoppe |0 P:(DE-HGF)0 |b 0 |e Corresponding author |
| 111 | 2 | _ | |a Fourth Conference on Research Software Engineering in Germany, deRSE24 |c Würzburg |d 2024-03-05 - 2024-03-07 |w Germany |
| 245 | _ | _ | |a Engineering a large-scale data analytics and array computing library for research: Heat |
| 260 | _ | _ | |c 2025 |b Electronic Communications of the EASST |
| 295 | 1 | 0 | |a Electronic Communications of the EASST |
| 300 | _ | _ | |a 1-26 |
| 336 | 7 | _ | |a CONFERENCE_PAPER |2 ORCID |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Conference Paper |2 DataCite |
| 336 | 7 | _ | |a Contribution to a conference proceedings |b contrib |m contrib |0 PUB:(DE-HGF)8 |s 1765464821_29041 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a Contribution to a book |0 PUB:(DE-HGF)7 |2 PUB:(DE-HGF) |m contb |
| 520 | _ | _ | |a 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. |
| 536 | _ | _ | |a 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5112 |c POF4-511 |f POF IV |x 0 |
| 536 | _ | _ | |a 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5111 |c POF4-511 |f POF IV |x 1 |
| 536 | _ | _ | |a SLNS - SimLab Neuroscience (Helmholtz-SLNS) |0 G:(DE-Juel1)Helmholtz-SLNS |c Helmholtz-SLNS |x 2 |
| 588 | _ | _ | |a Dataset connected to DataCite |
| 650 | _ | 7 | |a Multi-dimensional Arrays |2 Other |
| 650 | _ | 7 | |a Machine learning |2 Other |
| 650 | _ | 7 | |a Data Science |2 Other |
| 650 | _ | 7 | |a Data analytics |2 Other |
| 650 | _ | 7 | |a High-Performance Computing |2 Other |
| 650 | _ | 7 | |a Parallel Computing |2 Other |
| 650 | _ | 7 | |a GPUs |2 Other |
| 650 | _ | 7 | |a Big Data |2 Other |
| 650 | _ | 7 | |a Research Software |2 Other |
| 700 | 1 | _ | |a Gutiérrez Hermosillo Muriedas, J. P. |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Tarnawa, Michael |0 P:(DE-Juel1)178977 |b 2 |
| 700 | 1 | _ | |a Philipp, Knechtges |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Hagemeier, Björn |0 P:(DE-Juel1)132123 |b 4 |
| 700 | 1 | _ | |a Krajsek, Kai |0 P:(DE-Juel1)129347 |b 5 |
| 700 | 1 | _ | |a Alexander, Rüttgers |0 P:(DE-HGF)0 |b 6 |
| 700 | 1 | _ | |a Markus, Götz |0 P:(DE-HGF)0 |b 7 |
| 700 | 1 | _ | |a Comito, Claudia |0 P:(DE-Juel1)174573 |b 8 |
| 773 | _ | _ | |a 10.14279/eceasst.v83.2626 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1044789/files/manuscript.pdf |y OpenAccess |
| 909 | C | O | |o oai:juser.fz-juelich.de:1044789 |p openaire |p open_access |p VDB |p driver |p dnbdelivery |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)178977 |
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| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |9 G:(DE-HGF)POF4-5112 |x 0 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |9 G:(DE-HGF)POF4-5111 |x 1 |
| 914 | 1 | _ | |y 2025 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 915 | _ | _ | |a Creative Commons Attribution CC BY 4.0 |0 LIC:(DE-HGF)CCBY4 |2 HGFVOC |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)JSC-20090406 |k JSC |l Jülich Supercomputing Center |x 0 |
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