Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/30473
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Programming Languages for Data-Intensive HPC Applications: a Systematic Mapping Study
Author(s): 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
Contact Email: andrea.bracciali@stir.ac.uk
Keywords: High Performance Computing (HPC)
Big DataData-Intensive Applications
Programming Languages
Domain-Specific Language (DSL)
General-Purpose Language (GPL)
Systematic Mapping Study (SMS)
Issue Date: Mar-2020
Citation: 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. https://doi.org/10.1016/j.parco.2019.102584
Abstract: 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.
DOI Link: 10.1016/j.parco.2019.102584
Rights: This 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/
Notes: Additional co-authors: Sabri Pllana, Ana Respício, José Simão, Luís Veiga, Ari Visa
Licence URL(s): http://creativecommons.org/licenses/by-nc-nd/4.0/

Files in This Item:
File Description SizeFormat 
chipsetSLR.pdfFulltext - Accepted Version925.99 kBAdobe PDFUnder Embargo until 2021-05-09    Request a copy

Note: If any of the files in this item are currently embargoed, you can request a copy directly from the author by clicking the padlock icon above. However, this facility is dependent on the depositor still being contactable at their original email address.



This item is protected by original copyright



A file in this item is licensed under a Creative Commons License Creative Commons

Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

If you believe that any material held in STORRE infringes copyright, please contact library@stir.ac.uk providing details and we will remove the Work from public display in STORRE and investigate your claim.