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http://hdl.handle.net/10362/132902| Título: | Programming languages for data-Intensive HPC applications: A systematic mapping study |
| Autor: | 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 |
| Palavras-chave: | 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 |
| Data: | 1-Mar-2020 |
| Citação: | 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., Pllana, S., Respício, A., Simão, J., Veiga, L., & Visa, A. (2020). Programming languages for data-Intensive HPC applications: A systematic mapping study. Parallel Computing, 91, Article 102584. https://doi.org/10.1016/j.parco.2019.102584 |
| Resumo: | 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. |
| Descrição: | 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. |
| Peer review: | yes |
| URI: | http://hdl.handle.net/10362/132902 |
| DOI: | https://doi.org/10.1016/j.parco.2019.102584 |
| ISSN: | 0167-8191 |
| Aparece nas colecções: | FCT: DI - Artigos em revista internacional com arbitragem científica |
Ficheiros deste registo:
| Ficheiro | Descrição | Tamanho | Formato | |
|---|---|---|---|---|
| Chipset_SMS_text_in_British_English_please_.pdf | 2,12 MB | Adobe PDF | Ver/Abrir |
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