Engineering a large-scale data analytics and array computing library for research: Heat
DOI:
https://doi.org/10.14279/eceasst.v83.2626Keywords:
Multi-dimensional Arrays, Machine learning, Data Science, Data analytics, High-Performance Computing, Parallel Computing, GPUs, Big Data, Research SoftwareAbstract
Heat is a Python library for massively-parallel and GPU-accelerated array computing and machine learning. It is developed by researchers for researchers, with the ultimate goal to make multi-dimensional array processing and machine learning for scientists (almost) as easy on a supercomputer as it is on a workstation with NumPy or scikit-learn. This paper highlights the relevance of this project to the research software engineering community by giving a short, but illustrative overview of Heat and discusses its role in the context of related libraries with a specific focus on its research software aspects.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Fabian Hoppe, Juan Pedro Gutiérrez Hermosillo Muriedas, Michael Tarnawa, Philipp Knechtges, Björn Hagemeier, Kai Krajsek, Alexander Rüttgers, Markus Götz, Claudia Comito

This work is licensed under a Creative Commons Attribution 4.0 International License.