HPC Jobs Classification and Resource Prediction to Minimize Job Failures
Why this work is in the frame
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Bibliographic record
Abstract
In this work, we focus on HPC job classification and resource prediction. HPC users at Concordia University come from different departments, and not all of them have an IT background. One of the most challenging issues for users is how to properly estimate and request sufficient cluster resources and wall clock time for their jobs. Additionally, when a job terminates before its wall clock time limit has expired, that job continues to reserve HPC resources and blocks other jobs from using them until the wall clock time expires. Therefore, we propose a module that can predict whether a job will fail and that classifies job failure types. Based on this classification, we adjust the job submission parameters to avoid job failures and to prevent the misallocation of resources. Our proposed model reduces the job failure ratio by 92%.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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