Sun Grid Engine Package for OSCAR - A Google Summer Of Code 2005 Project
Bibliographic record
Abstract
Software deployment and maintenance is a hard problem, even more so when clusters and grids are involved. Open Source Cluster Application Resources (OSCAR) toolkit is used to install and maintain computing clusters. Grid and cluster software (such as Sun Grid Engine [SGE]) are often complex and huge. SGE is an open-source, community effort sponsored by Sun Microsystems, which offers distributed resource management via powerful queue configuration and job control capabilities. With the increasing adoption of clusters and grid computing, demand grows for mechanisms that facilitate easier installation and maintenance for popular software in this area. Adding SGE support in OSCAR allows cluster owners to realize maximum job throughput and utilize idling CPU cycles. As part of a winning proposal in Google’s Summer of Code 2005, we have developed a package for SGE as part of the OSCAR framework. This project was done with help from open source developers in SGE and OSCAR communities. As a result, a SGE package has been created, tested and made publicly available via OSCAR’s software repository. In this paper, we present the internals of and our experiences with this effort.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.066 | 0.052 |
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".