Lessons Learned in a Decade of Research Software Engineering GPU Applications

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,...

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Autores principales: van Werkhoven, Ben, Palenstijn, Willem Jan, Sclocco, Alessio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7304729/
http://dx.doi.org/10.1007/978-3-030-50436-6_29
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author van Werkhoven, Ben
Palenstijn, Willem Jan
Sclocco, Alessio
author_facet van Werkhoven, Ben
Palenstijn, Willem Jan
Sclocco, Alessio
author_sort van Werkhoven, Ben
collection PubMed
description 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.
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spelling pubmed-73047292020-06-22 Lessons Learned in a Decade of Research Software Engineering GPU Applications van Werkhoven, Ben Palenstijn, Willem Jan Sclocco, Alessio Computational Science – ICCS 2020 Article 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. 2020-05-25 /pmc/articles/PMC7304729/ http://dx.doi.org/10.1007/978-3-030-50436-6_29 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
van Werkhoven, Ben
Palenstijn, Willem Jan
Sclocco, Alessio
Lessons Learned in a Decade of Research Software Engineering GPU Applications
title Lessons Learned in a Decade of Research Software Engineering GPU Applications
title_full Lessons Learned in a Decade of Research Software Engineering GPU Applications
title_fullStr Lessons Learned in a Decade of Research Software Engineering GPU Applications
title_full_unstemmed Lessons Learned in a Decade of Research Software Engineering GPU Applications
title_short Lessons Learned in a Decade of Research Software Engineering GPU Applications
title_sort lessons learned in a decade of research software engineering gpu applications
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7304729/
http://dx.doi.org/10.1007/978-3-030-50436-6_29
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