GPU Job Scheduler with Deep Reinforcement Learning
Bibliographic record
Abstract
We present Proximity-DRL, a novel method for GPU job assignment in a GPU cluster using Deep Reinforcement Learning (DRL). Proximity-DRL considers the spatial relationships of GPUs during GPU allocation for each job, which effectively reduces the communication cost among the GPUs assigned to the job. In addition, Proximity-DRL uses a sequential decision-making process to allocate GPUs for a job, which effectively reduces the action space of the DRL model and leads to better performance than DRL models that select multiple GPUs in a single decision-making step. Trained using the Proximal Policy Optimization (PPO) algorithm to facilitate multi-GPU selection, Proximity-DRL demonstrates superior performance in optimizing average Job Completion Time (JCT), makespan, and communication costs compared to several popular heuristic schedulers and a DRL-based scheduler, highlighting its potential to speed up distributed training of deep neural network models.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".