After years of using Graphics Processing Units (GPUs) to accelerate scientific applications in fields as varied as tomography, computer vision, climate modeling, digital forensics, geospatial databases, particle physics, radio astronomy, and localization microscopy, we noticed a number of technical, socio-technical, and non-technical challenges that Research Software Engineers (RSEs) may run into. While some of these challenges, such as managing different programming languages within a project, or having to deal with different memory spaces, are common to all software projects involving GPUs, others are more typical of scientific software projects. Among these challenges we include changing resolutions or scales, maintaining an application over time and making it sustainable, and evaluating both the obtained results and the achieved performance.

GPU Computing, Research software, Research software engineering, Software engineering
dx.doi.org/10.1007/978-3-030-50436-6_29
Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence
20th International Conference, Amsterdam, The Netherlands, June 3–5, 2020
Centrum Wiskunde & Informatica, Amsterdam, The Netherlands

van Werkhoven, B, Palenstijn, W.J, & Sclocco, A. (2020). Lessons learned in a decade of research software engineering gpu applications. In Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence. doi:10.1007/978-3-030-50436-6_29