Contribution to a conference proceedings/Contribution to a book

FZJ-2025-03345

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Engineering a large-scale data analytics and array computing library for research: Heat

Fabian, H. (Corresponding author) ; Gutiérrez Hermosillo Muriedas, J. P. ; Tarnawa, M.FZJ* ; Philipp, K. ; Hagemeier, B.FZJ* ; Krajsek, K.FZJ* ; Alexander, R. ; Markus, G. ; Comito, C.FZJ*

2025
Electronic Communications of the EASST

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Please use a persistent id in citations: doi:10.14279/eceasst.v83.2626  doi:10.14279/ECEASST.V83.2626  doi:10.34734/FZJ-2025-03345

Abstract: 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.

Keyword(s): Multi-dimensional Arrays ; Machine learning ; Data Science ; Data analytics ; High-Performance Computing ; Parallel Computing ; GPUs ; Big Data ; Research Software

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) (POF4-511)
2. 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) (POF4-511)
3. SLNS - SimLab Neuroscience (Helmholtz-SLNS) (Helmholtz-SLNS)

Appears in the scientific report 2025

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