Programming languages for data-Intensive HPC applications: A systematic mapping study

Detalhes bibliográficos
Autor(a) principal: Amaral, Vasco
Data de Publicação: 2020
Outros Autores: Norberto, Beatriz, Goulão, Miguel, Aldinucci, Marco, Benkner, Siegfried, Bracciali, Andrea, Carreira, Paulo, Celms, Edgars, Correia, Luís, Grelck, Clemens, Karatza, Helen, Kessler, Christoph, Kilpatrick, Peter, Martiniano, Hugo, Mavridis, Ilias, Pllana, Sabri, Respício, Ana, Simão, José, Veiga, Luís, Visa, Ari
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/132902
Resumo: This work is a result of activities from COST Action 10406 High -Performance Modelling and Simulation for Big Data Applications (cHiPSet), funded by the European Cooperation in Science and Technology. FCT, Portugal for grants: NOVA LINCS Research Laboratory Ref. UID/ CEC/ 04516/ 2019); INESC-ID Ref. UID/CEC/50021/2019; BioISI Ref. UID/MULTI/04046/2103; LASIGE Research Unit Ref. UID/CEC/00408/ 2019.
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spelling Programming languages for data-Intensive HPC applications: A systematic mapping studyBig dataData-intensive applicationsDomain-Specific language (DSL)General-Purpose language (GPL)High performance computing (HPC)Programming languagesSystematic mapping study (SMS)SoftwareTheoretical Computer ScienceHardware and ArchitectureComputer Networks and CommunicationsComputer Graphics and Computer-Aided DesignArtificial IntelligenceThis work is a result of activities from COST Action 10406 High -Performance Modelling and Simulation for Big Data Applications (cHiPSet), funded by the European Cooperation in Science and Technology. FCT, Portugal for grants: NOVA LINCS Research Laboratory Ref. UID/ CEC/ 04516/ 2019); INESC-ID Ref. UID/CEC/50021/2019; BioISI Ref. UID/MULTI/04046/2103; LASIGE Research Unit Ref. UID/CEC/00408/ 2019.A major challenge in modelling and simulation is the need to combine expertise in both software technologies and a given scientific domain. When High-Performance Computing (HPC) is required to solve a scientific problem, software development becomes a problematic issue. Considering the complexity of the software for HPC, it is useful to identify programming languages that can be used to alleviate this issue. Because the existing literature on the topic of HPC is very dispersed, we performed a Systematic Mapping Study (SMS) in the context of the European COST Action cHiPSet. This literature study maps characteristics of various programming languages for data-intensive HPC applications, including category, typical user profiles, effectiveness, and type of articles. We organised the SMS in two phases. In the first phase, relevant articles are identified employing an automated keyword-based search in eight digital libraries. This lead to an initial sample of 420 papers, which was then narrowed down in a second phase by human inspection of article abstracts, titles and keywords to 152 relevant articles published in the period 2006–2018. The analysis of these articles enabled us to identify 26 programming languages referred to in 33 of relevant articles. We compared the outcome of the mapping study with results of our questionnaire-based survey that involved 57 HPC experts. The mapping study and the survey revealed that the desired features of programming languages for data-intensive HPC applications are portability, performance and usability. Furthermore, we observed that the majority of the programming languages used in the context of data-intensive HPC applications are text-based general-purpose programming languages. Typically these have a steep learning curve, which makes them difficult to adopt. We believe that the outcome of this study will inspire future research and development in programming languages for data-intensive HPC applications.NOVALincsDI - Departamento de InformáticaRUNAmaral, VascoNorberto, BeatrizGoulão, MiguelAldinucci, MarcoBenkner, SiegfriedBracciali, AndreaCarreira, PauloCelms, EdgarsCorreia, LuísGrelck, ClemensKaratza, HelenKessler, ChristophKilpatrick, PeterMartiniano, HugoMavridis, IliasPllana, SabriRespício, AnaSimão, JoséVeiga, LuísVisa, Ari2022-02-14T23:24:14Z2020-03-012020-03-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/132902eng0167-8191PURE: 16350119https://doi.org/10.1016/j.parco.2019.102584info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:11:33Zoai:run.unl.pt:10362/132902Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:47:37.494736Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Programming languages for data-Intensive HPC applications: A systematic mapping study
title Programming languages for data-Intensive HPC applications: A systematic mapping study
spellingShingle Programming languages for data-Intensive HPC applications: A systematic mapping study
Amaral, Vasco
Big data
Data-intensive applications
Domain-Specific language (DSL)
General-Purpose language (GPL)
High performance computing (HPC)
Programming languages
Systematic mapping study (SMS)
Software
Theoretical Computer Science
Hardware and Architecture
Computer Networks and Communications
Computer Graphics and Computer-Aided Design
Artificial Intelligence
title_short Programming languages for data-Intensive HPC applications: A systematic mapping study
title_full Programming languages for data-Intensive HPC applications: A systematic mapping study
title_fullStr Programming languages for data-Intensive HPC applications: A systematic mapping study
title_full_unstemmed Programming languages for data-Intensive HPC applications: A systematic mapping study
title_sort Programming languages for data-Intensive HPC applications: A systematic mapping study
author Amaral, Vasco
author_facet Amaral, Vasco
Norberto, Beatriz
Goulão, Miguel
Aldinucci, Marco
Benkner, Siegfried
Bracciali, Andrea
Carreira, Paulo
Celms, Edgars
Correia, Luís
Grelck, Clemens
Karatza, Helen
Kessler, Christoph
Kilpatrick, Peter
Martiniano, Hugo
Mavridis, Ilias
Pllana, Sabri
Respício, Ana
Simão, José
Veiga, Luís
Visa, Ari
author_role author
author2 Norberto, Beatriz
Goulão, Miguel
Aldinucci, Marco
Benkner, Siegfried
Bracciali, Andrea
Carreira, Paulo
Celms, Edgars
Correia, Luís
Grelck, Clemens
Karatza, Helen
Kessler, Christoph
Kilpatrick, Peter
Martiniano, Hugo
Mavridis, Ilias
Pllana, Sabri
Respício, Ana
Simão, José
Veiga, Luís
Visa, Ari
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv NOVALincs
DI - Departamento de Informática
RUN
dc.contributor.author.fl_str_mv Amaral, Vasco
Norberto, Beatriz
Goulão, Miguel
Aldinucci, Marco
Benkner, Siegfried
Bracciali, Andrea
Carreira, Paulo
Celms, Edgars
Correia, Luís
Grelck, Clemens
Karatza, Helen
Kessler, Christoph
Kilpatrick, Peter
Martiniano, Hugo
Mavridis, Ilias
Pllana, Sabri
Respício, Ana
Simão, José
Veiga, Luís
Visa, Ari
dc.subject.por.fl_str_mv Big data
Data-intensive applications
Domain-Specific language (DSL)
General-Purpose language (GPL)
High performance computing (HPC)
Programming languages
Systematic mapping study (SMS)
Software
Theoretical Computer Science
Hardware and Architecture
Computer Networks and Communications
Computer Graphics and Computer-Aided Design
Artificial Intelligence
topic Big data
Data-intensive applications
Domain-Specific language (DSL)
General-Purpose language (GPL)
High performance computing (HPC)
Programming languages
Systematic mapping study (SMS)
Software
Theoretical Computer Science
Hardware and Architecture
Computer Networks and Communications
Computer Graphics and Computer-Aided Design
Artificial Intelligence
description This work is a result of activities from COST Action 10406 High -Performance Modelling and Simulation for Big Data Applications (cHiPSet), funded by the European Cooperation in Science and Technology. FCT, Portugal for grants: NOVA LINCS Research Laboratory Ref. UID/ CEC/ 04516/ 2019); INESC-ID Ref. UID/CEC/50021/2019; BioISI Ref. UID/MULTI/04046/2103; LASIGE Research Unit Ref. UID/CEC/00408/ 2019.
publishDate 2020
dc.date.none.fl_str_mv 2020-03-01
2020-03-01T00:00:00Z
2022-02-14T23:24:14Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/132902
url http://hdl.handle.net/10362/132902
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0167-8191
PURE: 16350119
https://doi.org/10.1016/j.parco.2019.102584
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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