Bibliographic record
Abstract
High performance computing architectures continue to evolve along several dimensions. These changes are driven by the demand for more complex simulations and the ability to create, handle, and analyse ever growing volumes of data. This paper will focus on the state of the art in computer architectures designed to server large academic research communities in countries around the world. Prominent examples will include NCI (Australia), LRZ (Germany), and SciNet (Canada). Besides these examples that are in place, trends in technology will be summarized, to show what can reasonably be expected in the next five years. This will include expected advances in many of the key areas of computer architecture, including processors, memory, networking, and storage. Particular emphasis will be placed on the rapidly evolving area of storage technology. ABOUT THE AUTHOR(S) Jeff Zais recently joined NeSI and NIWA as the Senior High Performance Computing Architect and Science Advisor. His academic background includes a B.S. degree from the University of Wisconsin, and M.S. and Ph.D. degrees from Stanford University in Aerospace Engineering. Professional experience includes technical and management roles at Ford Aerospace, Cray Research, IBM, and Lenovo, focused on application performance and system architecture.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.073 | 0.039 |
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".