The Big Ideas for HPC Education: From Existing Needs in High-Performance Computing Training to Recommendations for Instructional Design

This white paper presents a comprehensive investigation into the current state and future directions of High-Performance Computing (HPC) education, addressing the critical gap between industry demand for HPC-skilled professionals and the inadequate educational offerings. Through expert consultation, literature review, overviews on the existing programs at Italian and European level, and survey methodology, this study delivers three key contributions to advance HPC education globally. The research reveals persistent and widespread skill gaps across all stakeholder groups in the HPC ecosystem. Foundational deficiencies include the absence of core parallel programming concepts such as concurrency, parallel programming patterns, performance optimization skills, and understanding of HPC system architectures in most undergraduate curricula. Technical gaps encompass insufficient training in software optimization tools, data-intensive computing workflows, and emerging technologies like heterogeneous computing and containerization. The analysis identifies distinct training needs where Computer Science students lack integration of HPC fundamentals in core curricula, systematic optimization training, and exposure to real-world data workflows, while domain scientists and researchers suffer from low computational literacy, overreliance on domain-specific tools without understanding underlying principles, and late exposure to computational problem solving. Academic institutions face curricular fragmentation, limited educator expertise, underemphasis on reproducibility practices, and inadequate access to HPC infrastructure for hands-on learning. Industry professionals encounter barriers including absence of HPC foundations in formal education, misaligned training formats, lack of standardized certification, and insufficient knowledge transfer mechanisms within organizations. Cross-cutting gaps affect all profiles through underrepresentation of emerging technologies like AI-HPC integration and quantum computing, inadequate data management and governance training, limited focus on sustainability and societal impact, poor interdisciplinary communication skills, and pedagogical methods that fail to engage diverse learners effectively. Drawing from educational frameworks in science and computer science, this study proposes ten fundamental "Big Ideas" of HPC that should guide curriculum design and instructional approaches. These include understanding that computation has physical limits and we can push them, recognizing that parallelism is a key to speed and scale, understanding that performance comes from matching problem, algorithm, and architecture, knowing that decomposition is fundamental, realizing that communication can be more expensive than computation, recognizing that HPC requires collaboration between software and hardware, understanding that scientific discovery and engineering innovation depend on HPC, knowing that not all problems scale and some never will, appreciating that reproducibility and precision matter at scale, and understanding that HPC skills are transferable and evolving. These big ideas provide a stable conceptual framework that transcends rapidly changing technologies, enabling educators to design coherent curricula and helping learners understand HPC as an integrated discipline rather than a collection of disconnected tools. In the final section, the study presents comprehensive recommendations for transforming HPC education and training programs. Universities should embed HPC fundamentals across relevant degree programs rather than treating it as a specialized elective, ensuring early exposure to parallel thinking and computational literacy while adopting the "big ideas" approach to create structured, coherent curricula that emphasize enduring principles over transient technologies. Building capacity through HPC education communities of practice, faculty training workshops, and shared teaching resources addresses the critical shortage of qualified instructors, while ensuring access to hands-on HPC environments through cloud platforms, educational clusters, or partnerships with HPC centers provides essential experiential learning for understanding scalability, performance optimization, and system management. The recommendations emphasize tailoring training to different learner profiles while encouraging cross-disciplinary collaboration, recognizing that computer scientists need domain exposure while domain scientists need computational foundations. Strengthening connections through internships, joint projects, and industry expert involvement ensures curricula remain relevant to evolving workplace needs and emerging technologies. The approach integrates data management, AI governance, sustainability considerations, and emerging technology trends such as quantum computing and digital twins to prepare professionals for the converging computational landscape, while supporting diversity initiatives, providing multiple entry points for learners with varying backgrounds, and ensuring equitable access to training resources and opportunities. This research establishes a foundation for systematic transformation of HPC education, moving from ad-hoc, fragmented approaches toward coherent, principle-based training that can scale to meet growing workforce demands while adapting to technological evolution. The framework provides educators, policymakers, and industry leaders with actionable guidance for developing effective HPC education programs that bridge the current skills gap and prepare professionals for the computational challenges of the future.

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