Engineering a large-scale data analytics and array computing library for research: Heat - Hoppe, Fabian; Gutiérrez Hermosillo Muriedas, Juan Pedro; Tarnawa, Michael; Knechtges, Philipp; Hagemeier, Björn; Krajsek, Kai; Rüttgers, Alexander; Götz, Markus; Comito, Claudia (Electronic Communications of the EASST, 2025)
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.