External Resources: Clouds and HPCs for the expansion of the ATLAS production system at the Tokyo regional analysis center
Bibliographic record
Abstract
The Tokyo regional analysis center at the International Center for Elementary Particle Physics, the University of Tokyo, is one of the Tier 2 sites for the ATLAS experiment in the Worldwide LHC Computing Grid. The current system has 10,752 CPU cores and 16 PB disk storage. CERN plans the HighLuminosity LHC starting from 2026, which increases the peak luminosity by 5 times compared to the present value at the LHC. A shortage of computing resources for the high-luminosity LHC is expected with the current system. Therefore new ideas to expand the system are necessary. To expand the ATLAS production system at the Tokyo regional analysis center, R&D using external resources has been launched. One kind of external resources is a commercial cloud resource, such as Google Cloud Platform. Another resource is the HighPerformance Computer (HPC) at the University of Tokyo. In this contribution, the current status of the R&D, the systems for these resources, and comparisons of the cost are reported.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.014 |
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".