Local Queueing-Based Data-Driven Task Scheduling for Multicore Systems
Bibliographic record
Abstract
Nowadays, multicore systems are widely used in high performance computing. Many algorithms have been proposed to enhance the system performance by load balancing or concurrent scheduling to reduce the execution time of applications. However, task scheduling on multicore systems is still an open issue, which needs to be analyzed to fully utilize the processing capacity and achieve low processing latencies. In order to tackle the inefficient utilization of CPU cores, a queueing-based data-driven task scheduling scheme, which focuses on local parallel computing, is introduced in this paper. In this scheduling scheme, multi-queue management is proposed for dynamic task scheduling to target a full utilization of local CPU cores when input tasks can keep them fully used. Furthermore, the preemption technique is applied to guarantee that high priority tasks will not be blocked by low priority tasks. Our solution can be combined with other algorithms taking into account earliest finish time or critical path to generate better results. Thus CPU core utilization can be improved while minimizing the makespan of high priority DAGs. Finally, simulations are carried out to verify the proposed task scheduling scheme. The reported results confirm its viability and efficiency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".