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Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/30473
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dc.contributor.authorAmaral, Vascoen_UK
dc.contributor.authorNorberto, Beatrizen_UK
dc.contributor.authorGoulão, Miguelen_UK
dc.contributor.authorAldinucci, Marcoen_UK
dc.contributor.authorBenkner, Siegfrieden_UK
dc.contributor.authorBracciali, Andreaen_UK
dc.contributor.authorCarreira, Pauloen_UK
dc.contributor.authorCelms, Edgarsen_UK
dc.contributor.authorCorreia, Luísen_UK
dc.contributor.authorGrelck, Clemensen_UK
dc.contributor.authorKaratza, Helenen_UK
dc.contributor.authorKessler, Christophen_UK
dc.contributor.authorKilpatrick, Peteren_UK
dc.contributor.authorMartiniano, Hugoen_UK
dc.contributor.authorMavridis, Iliasen_UK
dc.date.accessioned2019-11-19T01:02:08Z-
dc.date.available2019-11-19T01:02:08Z-
dc.date.issued2020-03en_UK
dc.identifier.other102584en_UK
dc.identifier.urihttp://hdl.handle.net/1893/30473-
dc.description.abstractA 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.en_UK
dc.language.isoenen_UK
dc.publisherElsevier BVen_UK
dc.relationAmaral V, Norberto B, Goulão M, Aldinucci M, Benkner S, Bracciali A, Carreira P, Celms E, Correia L, Grelck C, Karatza H, Kessler C, Kilpatrick P, Martiniano H & Mavridis I (2020) Programming Languages for Data-Intensive HPC Applications: a Systematic Mapping Study. Parallel Computing, 91, Art. No.: 102584. https://doi.org/10.1016/j.parco.2019.102584en_UK
dc.rightsThis item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study. Accepted refereed manuscript of: Amaral V, Norberto B, Goulão M, Aldinucci M, Benkner S, Bracciali A, Carreira P, Celms E, Correia L, Grelck C, Karatza H, Kessler C, Kilpatrick P, Martiniano H & Mavridis I (2020) Programming Languages for Data-Intensive HPC Applications: a Systematic Mapping Study. Parallel Computing, 91, Art. No.: 102584. DOI: https://doi.org/10.1016/j.parco.2019.102584 © 2019, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/en_UK
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en_UK
dc.subjectHigh Performance Computing (HPC)en_UK
dc.subjectBig DataData-Intensive Applicationsen_UK
dc.subjectProgramming Languagesen_UK
dc.subjectDomain-Specific Language (DSL)en_UK
dc.subjectGeneral-Purpose Language (GPL)en_UK
dc.subjectSystematic Mapping Study (SMS)en_UK
dc.titleProgramming Languages for Data-Intensive HPC Applications: a Systematic Mapping Studyen_UK
dc.typeJournal Articleen_UK
dc.rights.embargodate2021-05-09en_UK
dc.rights.embargoreason[chipsetSLR.pdf] Publisher requires embargo of 18 months after formal publication.en_UK
dc.identifier.doi10.1016/j.parco.2019.102584en_UK
dc.citation.jtitleParallel Computingen_UK
dc.citation.issn0167-8191en_UK
dc.citation.volume91en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusAM - Accepted Manuscripten_UK
dc.author.emailandrea.bracciali@stir.ac.uken_UK
dc.citation.date08/11/2019en_UK
dc.description.notesAdditional co-authors: Sabri Pllana, Ana Respício, José Simão, Luís Veiga, Ari Visaen_UK
dc.contributor.affiliationNew University of Lisbonen_UK
dc.contributor.affiliationNew University of Lisbonen_UK
dc.contributor.affiliationNew University of Lisbonen_UK
dc.contributor.affiliationUniversity of Torino, Italyen_UK
dc.contributor.affiliationUniversity of Viennaen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationUniversity of Lisbonen_UK
dc.contributor.affiliationUniversity of Latviaen_UK
dc.contributor.affiliationUniversity of Lisbonen_UK
dc.contributor.affiliationUniversity of Amsterdamen_UK
dc.contributor.affiliationAristotle University of Thessalonikien_UK
dc.contributor.affiliationLinkoping Universityen_UK
dc.contributor.affiliationQueen's University Belfasten_UK
dc.contributor.affiliationUniversity of Lisbonen_UK
dc.contributor.affiliationAristotle University of Thessalonikien_UK
dc.identifier.isiWOS:000510110400004en_UK
dc.identifier.scopusid2-s2.0-85076201522en_UK
dc.identifier.wtid1482625en_UK
dc.contributor.orcid0000-0003-3791-5151en_UK
dc.contributor.orcid0000-0003-0668-6552en_UK
dc.contributor.orcid0000-0002-6520-2047en_UK
dc.contributor.orcid0000-0003-1451-9260en_UK
dc.contributor.orcid0000-0003-2439-1168en_UK
dc.contributor.orcid0000-0003-3003-1388en_UK
dc.contributor.orcid0000-0001-5241-0026en_UK
dc.contributor.orcid0000-0003-0818-8979en_UK
dc.contributor.orcid0000-0003-2490-8913en_UK
dc.date.accepted2019-10-29en_UK
dcterms.dateAccepted2019-10-29en_UK
dc.date.filedepositdate2019-11-18en_UK
rioxxterms.apcnot requireden_UK
rioxxterms.typeJournal Article/Reviewen_UK
rioxxterms.versionAMen_UK
local.rioxx.authorAmaral, Vasco|0000-0003-3791-5151en_UK
local.rioxx.authorNorberto, Beatriz|0000-0003-0668-6552en_UK
local.rioxx.authorGoulão, Miguel|en_UK
local.rioxx.authorAldinucci, Marco|en_UK
local.rioxx.authorBenkner, Siegfried|0000-0002-6520-2047en_UK
local.rioxx.authorBracciali, Andrea|0000-0003-1451-9260en_UK
local.rioxx.authorCarreira, Paulo|en_UK
local.rioxx.authorCelms, Edgars|en_UK
local.rioxx.authorCorreia, Luís|0000-0003-2439-1168en_UK
local.rioxx.authorGrelck, Clemens|0000-0003-3003-1388en_UK
local.rioxx.authorKaratza, Helen|en_UK
local.rioxx.authorKessler, Christoph|0000-0001-5241-0026en_UK
local.rioxx.authorKilpatrick, Peter|0000-0003-0818-8979en_UK
local.rioxx.authorMartiniano, Hugo|0000-0003-2490-8913en_UK
local.rioxx.authorMavridis, Ilias|en_UK
local.rioxx.projectInternal Project|University of Stirling|https://isni.org/isni/0000000122484331en_UK
local.rioxx.freetoreaddate2021-05-09en_UK
local.rioxx.licencehttp://www.rioxx.net/licenses/under-embargo-all-rights-reserved||2021-05-08en_UK
local.rioxx.licencehttp://creativecommons.org/licenses/by-nc-nd/4.0/|2021-05-09|en_UK
local.rioxx.filenamechipsetSLR.pdfen_UK
local.rioxx.filecount1en_UK
local.rioxx.source0167-8191en_UK
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