6533b854fe1ef96bd12ae0aa

RESEARCH PRODUCT

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

Vasco AmaralHugo MartinianoPaulo CarreiraClemens GrelckPeter KilpatrickSabri PllanaMiguel GoulãoBeatriz NorbertoAri VisaLuis M. CorreiaAna RespícioHelen D. KaratzaLuís VeigaSiegfried BenknerIlias MavridisJosé SimãoEdgars CelmsChristoph KesslerAndrea BraccialiMarco Aldinucci

subject

Domain-Specific language (DSL)High performance computing (HPC)Computer scienceComputer Networks and CommunicationsBig data; Data-intensive applications; Domain-Specific language (DSL); General-Purpose language (GPL); High performance computing (HPC); Programming languages; Systematic mapping study (SMS)Systematic mapping study (SMS)Big dataData-intensive applicationsContext (language use)computer.software_genreTheoretical Computer ScienceSoftware portabilityBig dataSoftwareArtificial Intelligencebusiness.industryProgramming languageSoftware developmentGeneral-Purpose language (GPL)UsabilityProgramming languagesDigital libraryComputer Graphics and Computer-Aided DesignHardware and ArchitecturebusinesscomputerSoftware

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. 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. authorsversion published

yearjournalcountryeditionlanguage
2020-03-01
10.1016/j.parco.2019.102584https://hdl.handle.net/10362/132902