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Published May 20, 2019 | Version v1
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Companion data of a Systematic Mapping Study of Programming Languages for Data-Intensive HPC Applications

Authors/Creators

  • 1. Universidade Nova de Lisboa
  • 2. University of Torino
  • 3. University of Vienna
  • 4. University of Stirling
  • 5. Universidade de Lisboa
  • 6. University of Latvia
  • 7. University of Amsterdam
  • 8. Aristotle University of Thessaloniki
  • 9. Linkoping University
  • 10. Queens University Belfast
  • 11. Aristotle University Thessaloniki
  • 12. Linnaeus University
  • 13. Instituto superior de Engenharia de Lisboa

Description

As the current 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. This document is the data companion for a paper published elsewhere and presents a detailed list of the selected papers. Besides, the document also presents the form of our questionnaire-based survey that involved 28 HPC experts. 

 

Notes

This work results from COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications (cHiPSet), funded by the European Cooperation in Science and Technology.

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Companion___Chipset_SMS.pdf

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