An Empirical Analysis of High-Performance Computing Education in Germany

The growing importance of High-Performance Computing (HPC) requires the systematic integration of parallel programming and performance-oriented competencies into computational science curricula. Effective HPC education combines theoretical foundations with practical experience on real cluster infrastructures, enabling students to understand scalability, efficiency, and architectural differences between shared and distributed memory systems. However, cross-institutional evidence on how HPC education is implemented, and how curricula relate to locally available infrastructure, remains limited. We address this gap through a systematic empirical assessment of HPC education at 102 academic institutions in Germany. Based on module handbooks and course catalogs, we identified 178 HPC-related courses and evaluated their competency coverage and curricular placement. We additionally assessed local academic HPC cluster infrastructures with respect to availability, size, and documented accessibility for teaching. The results show that 67.6% of institutions offer at least one HPC-related course, but these offerings are predominantly elective modules at the master's level, with limited integration in bachelor's programs. Although 61.8% of institutions operate HPC clusters, only 23.0% explicitly document their availability for educational use, as infrastructures are mainly reserved for research. Statistical analysis indicates a significant association between restricted teaching access and reduced curricular emphasis on practical competencies such as resource management, cluster usage, parallel debugging, and performance analysis. Overall, the findings reveal a structural imbalance between theoretical instruction and the development of practical HPC competencies in German higher education.