A performance study of job management systems
Bibliographic record
Abstract
Abstract Job Management Systems (JMSs) efficiently schedule and monitor jobs in parallel and distributed computing environments. Therefore, they are critical for improving the utilization of expensive resources in high‐performance computing systems and centers, and an important component of Grid software infrastructure. With many JMSs available commercially and in the public domain, it is difficult to choose an optimum JMS for a given computing environment. In this paper, we present the results of the first empirical study of JMSs reported in the literature. Four commonly used systems, LSF, PBS Pro, Sun Grid Engine/CODINE, and Condor were considered. The study has revealed important strengths and weaknesses of these JMSs under different operational conditions. For example, LSF was shown to exhibit excellent throughput for a wide range of job types and submission rates. Alternatively, CODINE appeared to outperform other systems in terms of the average turn‐around time for small jobs, and PBS appeared to excel in terms of turn‐around time for relatively larger jobs. Copyright © 2004 John Wiley & Sons, Ltd.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".