Using ATLAS@Home to Exploit Extra CPU from Busy Grid Sites
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Grid computing typically provides most of the data processing resources for large high-energy physics experiments. However, typical grid sites are not fully utilized by regular workloads. To increase the CPU utilization of these grid sites, the ATLAS@Home volunteer computing framework can be used as a backfilling mechanism. Results show an extra 15–42% of CPU cycles can be exploited by backfilling grid sites running regular workloads, while the overall CPU utilization can remain over 0.9. Backfilling has no impact on the failure rate of the grid jobs, and the impact on the CPU efficiency of grid jobs varies from 0.02 to 0.11 depending on the configuration of the site. In addition, the throughput of backfill jobs in terms of CPU time per simulated event is the same as for resources dedicated to ATLAS@Home. This approach is sufficiently generic that it can easily be extended to other clusters.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it