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Mapping the national HPC ecosystem and training needs: The Greek paradigm

HPC is a key tool for processing and analyzing the constantly growing volume of data, from 64.2 zettabytes in 2020 to an expected 180 zettabytes in 2025 (1 zettabyte is equal to 1 trillion gigabytes). As such, HPC has a large number of application areas that range from climate change, monitoring and...

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Autores principales: Karozis, Stelios, Ziouvelou, Xenia, Karkaletsis, Vangelis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9931169/
https://www.ncbi.nlm.nih.gov/pubmed/36819975
http://dx.doi.org/10.1007/s11227-023-05080-y
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author Karozis, Stelios
Ziouvelou, Xenia
Karkaletsis, Vangelis
author_facet Karozis, Stelios
Ziouvelou, Xenia
Karkaletsis, Vangelis
author_sort Karozis, Stelios
collection PubMed
description HPC is a key tool for processing and analyzing the constantly growing volume of data, from 64.2 zettabytes in 2020 to an expected 180 zettabytes in 2025 (1 zettabyte is equal to 1 trillion gigabytes). As such, HPC has a large number of application areas that range from climate change, monitoring and mitigating planning to the production of safer and greener vehicles and treating COVID-19 pandemic to the advancement of knowledge in almost every scientific field and industrial domain. The current work presents an HPC Training Mapping Framework and the relevant findings and processed data of an online Training Needs Analysis (TNA) survey. The latter was used to map the training demands and gaps of existing skills and future ones. The participants consist of academia and industry and the data were utilized to find the profile of HPC user alongside the best training practices that are in need. It is found that in Greece during the year 2021, the stakeholder segment with the highest number of respondents was from academia and research with a total of 74%. The vast majority appear to have basic information accounting for 37% of the respondents. In terms of familiarity, users with intermediate familiarity with HPC represented 21% of respondents, followed by non-familiar users that accounted in total for 16.1. Advanced and highly advanced user segments account only for 8.6% and 7.4% accordingly. Overall, it is found that a: (1) fast-pace, (2) entry level, (3) applied HPC training but (4) not focused only on HPC, that will (5) provide some kind of certification, by the Greek HPC ecosystem.
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spelling pubmed-99311692023-02-16 Mapping the national HPC ecosystem and training needs: The Greek paradigm Karozis, Stelios Ziouvelou, Xenia Karkaletsis, Vangelis J Supercomput Article HPC is a key tool for processing and analyzing the constantly growing volume of data, from 64.2 zettabytes in 2020 to an expected 180 zettabytes in 2025 (1 zettabyte is equal to 1 trillion gigabytes). As such, HPC has a large number of application areas that range from climate change, monitoring and mitigating planning to the production of safer and greener vehicles and treating COVID-19 pandemic to the advancement of knowledge in almost every scientific field and industrial domain. The current work presents an HPC Training Mapping Framework and the relevant findings and processed data of an online Training Needs Analysis (TNA) survey. The latter was used to map the training demands and gaps of existing skills and future ones. The participants consist of academia and industry and the data were utilized to find the profile of HPC user alongside the best training practices that are in need. It is found that in Greece during the year 2021, the stakeholder segment with the highest number of respondents was from academia and research with a total of 74%. The vast majority appear to have basic information accounting for 37% of the respondents. In terms of familiarity, users with intermediate familiarity with HPC represented 21% of respondents, followed by non-familiar users that accounted in total for 16.1. Advanced and highly advanced user segments account only for 8.6% and 7.4% accordingly. Overall, it is found that a: (1) fast-pace, (2) entry level, (3) applied HPC training but (4) not focused only on HPC, that will (5) provide some kind of certification, by the Greek HPC ecosystem. Springer US 2023-02-15 2023 /pmc/articles/PMC9931169/ /pubmed/36819975 http://dx.doi.org/10.1007/s11227-023-05080-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Karozis, Stelios
Ziouvelou, Xenia
Karkaletsis, Vangelis
Mapping the national HPC ecosystem and training needs: The Greek paradigm
title Mapping the national HPC ecosystem and training needs: The Greek paradigm
title_full Mapping the national HPC ecosystem and training needs: The Greek paradigm
title_fullStr Mapping the national HPC ecosystem and training needs: The Greek paradigm
title_full_unstemmed Mapping the national HPC ecosystem and training needs: The Greek paradigm
title_short Mapping the national HPC ecosystem and training needs: The Greek paradigm
title_sort mapping the national hpc ecosystem and training needs: the greek paradigm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9931169/
https://www.ncbi.nlm.nih.gov/pubmed/36819975
http://dx.doi.org/10.1007/s11227-023-05080-y
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