Mapping the national HPC ecosystem and training needs: The Greek paradigm.

Area of Science:

• High-Performance Computing (HPC)
• Data Science
• Computational Science

Background:

• The exponential growth of data necessitates advanced computational tools like HPC.
• HPC applications span diverse fields including climate science, automotive engineering, and biomedical research.
• Understanding user skills and training needs is crucial for effective HPC adoption.

Purpose of the Study:

• To present an HPC Training Mapping Framework.
• To analyze training demands and skill gaps through an online Training Needs Analysis (TNA) survey.
• To identify the profile of HPC users and optimal training practices in Greece.

Main Methods:

• Conducted an online Training Needs Analysis (TNA) survey targeting academia and industry stakeholders in Greece.
• Collected and processed data to map existing and future training requirements.
• Analyzed respondent profiles based on familiarity and skill levels with HPC.

Main Results:

• Academia and research represented 74% of respondents in Greece (2021).
• The majority of users possess basic (37%) or intermediate (21%) familiarity with HPC; advanced users are scarce (8.6% advanced, 7.4% highly advanced).
• A significant portion of users (16.1%) are not familiar with HPC.

Conclusions:

• There is a need for fast-paced, entry-level, applied HPC training.
• Training should not be solely HPC-focused but encompass broader applications.
• Certification is a desired outcome for HPC training programs within the Greek ecosystem.