发表机构
Fulda University of Applied Sciences（富尔达应用科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
EduMPI Suite 通过自动化与可视化降低 MPI 性能分析门槛，三年实践表明早期引入能提升学生理解与接受度，应贯穿 MPI 教育。
AI 中文摘要
性能分析对于可扩展的 MPI 程序至关重要，然而工具和工作流的复杂性常常使其在并行编程课程中被推迟到课程末尾才讲授。EduMPI Suite 通过自动化集群执行、测量和近实时分析，并从第一个程序开始就可视化 MPI 通信，降低了这一入门门槛。本文介绍了将其整合到 HPC 教育中的三年经验报告。我们利用未发表的历史调查证据和 2025 年的个人问卷数据，追溯了性能分析从最后一周的附加内容转变为 MPI 开发中反复出现的元素的历程。早期的学生群体认为性能分析是不必要的、困难的或难以直观理解的。在最终的研究中，所有学生都建议在课程早期引入 EduMPI Suite。在 33 名学生中，有 29 至 33 名学生认为它在理解程序行为、解读通信、识别瓶颈、获取视觉反馈和操作工具方面优于传统工具。这些发现支持在 MPI 教育中贯穿性地嵌入性能分析，然后再将学习者过渡到专业环境。
英文摘要
Performance analysis is essential for scalable MPI programs, yet tool and workflow complexity often relegates it to the end of Parallel Programming courses. EduMPI Suite lowers this entry barrier by automating cluster execution, measurement, and near-real-time analysis while visualizing MPI communication from the first program. This paper presents a three-year experience report on its integration into HPC education. We trace the transition of performance analysis from a last-week add-on to a recurring element of MPI development, using unpublished historical survey evidence and individual 2025 questionnaire data. Earlier cohorts perceived performance analysis as unnecessary, difficult, or unintuitive. In the final study, all students recommended introducing EduMPI Suite early in the course. Of 33 students, 29-33 rated it higher than conventional tools for understanding program behavior, interpreting communication, identifying bottlenecks, obtaining visual feedback, and navigating the tool. These findings support embedding performance analysis throughout MPI education before transitioning learners to professional environments.
CommentsAccepted manuscript. Accepted for publication in the SC26 Workshops
