An Improved Job Co-Allocation Strategy in Multiple HPC Clusters
Why this work is in the frame
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Bibliographic record
Abstract
To more effectively use HPC clusters, co-allocating jobs across multiple clusters becomes an attractive possibility with the primary benefit being reduced turnaround time. This, ultimately, depends on the inter- cluster communication cost. In our previous research, we introduced a co-allocation strategy, MBAS, that made use of two threshold values to control allocation: one for control link saturation and another to control job splitting. In this paper, we examine the performance of MBAS. A simulation study concludes that assigning jobs with different priorities according to their communication patterns, and adjusting the threshold values for link saturation level control and chunk size control in splitting jobs, the MBAS co- allocation strategy can significantly improve both user' satisfaction (in terms of turn around time) and system resource utilization consistently, even for jobs having large communication requirements.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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