Companion data of a Systematic Mapping Study of Programming Languages for Data-Intensive HPC Applications

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. We also include the filled in questionnaires and raw data of the referred survey. To validate the SMS results we conducted a survey (in November 2018) with 28 HPC experts involved in the cHiPSet COST action to which we added, in October 2019, 29 HPC experts which were not involved in that COST action. Participants were recruited through convenience sampling, and contacted directly by the authors. In total, we received 57 filled survey forms.鉴于当前高性能计算（High Performance Computing，HPC）领域的相关文献分布极为零散，我们依托欧洲COST行动cHiPSet框架开展了一项系统映射研究（Systematic Mapping Study，SMS）。本研究梳理了面向数据密集型高性能计算应用的各类编程语言的特征，涵盖类别、典型用户画像、应用效能以及文献类型等维度。本次系统映射研究分为两个阶段实施：第一阶段，我们通过在8个数字图书馆中开展自动化关键词检索，初步获得420篇相关文献作为初始样本；第二阶段，通过人工评审文献的摘要、标题与关键词，将样本缩减至2006年至2018年间发表的152篇有效文献。通过对上述文献的分析，我们从33篇相关文献中识别出26种编程语言。本文档为已发表于其他平台的某篇论文的配套数据集资料，包含本次筛选入选的全部文献的详细清单；此外，文档还收录了本次问卷调研的问卷模板、已回收的有效问卷以及调研原始数据。为验证本次系统映射研究的结果，我们于2018年11月面向参与cHiPSet的COST行动的28名高性能计算专家开展调研，并于2019年10月新增29名未参与该COST行动的高性能计算专家作为补充调研对象。本次调研参与者通过便利抽样法招募，且由研究团队直接联系。最终我们共回收57份有效问卷。