EFFICIENT PARALLEL JOB SCHEDULING USING GANG SERVICE
Why this work is in the frame
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Bibliographic record
Abstract
Gang scheduling has been widely used as a practical solution to the dynamic parallel job scheduling problem. To overcome some of the limitations of traditional Gang scheduling algorithms, Concurrent Gang is proposed as a class of scheduling policies which allows the flexible and simultaneous scheduling of multiple parallel jobs. It hence improves the space sharing characteristics of Gang scheduling while preserving all other advantages. To provide a sound analysis of Concurrent Gang performance, a novel methodology based on the traditional concept of competitive ratio is also introduced. Dubbed dynamic competitive ratio, the new method is used to compare dynamic bin packing algorithms used in this paper. These packing algorithms apply to the Concurrent Gang scheduling of a workload generated by a statistical model. Moreover, dynamic competitive ratio is the figure of merit used to evaluate and compare packing strategies for job scheduling under multiple constraints. It will be shown that for the unidimensional case there is a small difference between the performance of best fit and first fit; first fit can hence be used without significant system degradation. For the multidimensional case, when memory is also considered, we concluded that the packing algorithm must try to balance the resource utilization in all dimensions simulataneously, instead of given priority to only one dimension of the problem.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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