Fifteen Years of International HPC Summer School
Bibliographic record
Abstract
Since 2010, the International HPC Summer School (IHPCSS) has trained more than 1,500 graduate students and early-career researchers in high-performance computing (HPC). Originally a European-US collaboration, it now includes Japan, Canada, Australia and South Africa, and serves around 80 students and 40 staff each year. This paper examines the evolution of the technical program, which initially focused on domain-specific scientific applications and MPI/OpenMP programming, but later expanded to include emerging technologies like GPU acceleration, Python for HPC, big data analytics, and AI/ML. It also discusses the challenges IHPCSS faced - technical, logistical, and demographic - and how they were addressed through real-time HPC access, mentorship, and adaptable sessions for mixed-skill audiences. IHPCSS continues to provide inclusive and high-quality trainings around the world by integrating new technologies and responding to participant feedback, while maintaining the core principles of HPC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.113 | 0.040 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".