Local Queueing-Based Data-Driven Task Scheduling for Multicore Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 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