Author manuscript, published in "International Conference on Computing in High Energy and Nuclear Physics (CHEP07), Victoria: Canada (2007)" DOI: 10.1088/1742-6596/119/6/062001 FERMILAB-PUB-07-462-CD DZero Data-Intensive Computing on the Open Science Grid
Bibliographic record
Abstract
Abstract. High energy physics experiments periodically reprocess data, in order to take advantage of improved understanding of the detector and the data processing code. Between February and May 2007, the DZero experiment has reprocessed a substantial fraction of its dataset. This consists of half a billion events, corresponding to about 100 TB of data, organized in 300,000 files. The activity utilized resources from sites around the world, including a dozen sites participating to the Open Science Grid consortium (OSG). About 1,500 jobs were run every day across the OSG, consuming and producing hundreds of Gigabytes of data. Access to OSG computing and storage resources was coordinated by the SAM-Grid system. This system organized job access to a complex topology of data queues and job scheduling to clusters, using a SAM-Grid to OSG job forwarding infrastructure. For the first time in the lifetime of the experiment, a data intensive production activity was managed on a general purpose grid, such as OSG. This paper describes the implications of using OSG, where all resources are granted following an opportunistic model, the challenges of operating a data intensive activity over such large computing infrastructure, and the lessons learned throughout the project. 1.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.655 | 0.502 |
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".