Efficient Task Scheduling and Allocation of GPU Resources in Clouds
Bibliographic record
Abstract
The growing demand for computational power in clouds is leading to the increasing adoption of Graphics Processing Units (GPUs). However, due to the high cost of GPUs, it is necessary to allocate these GPU resources efficiently. Cloud constraints such as isolation and multiple tenancy need also to be taken into account. An example of a challenge is how to avoid under-utilization when allocating resources under the constraint of isolation. Yet another example is how to schedule tasks and allocate GPU resources to them while respecting task requirements (e.g. completion deadlines) and fairness between the tenants. Unfortunately, none of the existing work satisfactorily addresses these challenges to the best of our knowledge. This paper proposes an algorithm for efficient GPU resource allocation in clouds. The algorithm relies on GPU multitasking methods supported by both hardware and software. It schedules tasks and allocates resources to the tasks efficiently while respecting isolation, fairness and task requirements. According to experimental results, our proposed algorithms outperform state-of-the-art solutions by up to 24.25% for the maximum normalized task completion time while significantly reducing GPU resource usage.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".