Efficient compile-time task scheduling for heterogeneous distributed computing systems
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Bibliographic record
Abstract
Efficient task scheduling is essential for obtaining high performance in heterogeneous distributed computing systems (or HeDCSs). Because of its key importance, several scheduling algorithms have been proposed in the literature, which are mainly for homogeneous processors. Few scheduling algorithms are developed for HeDCSs. In this paper, we present a novel task scheduling algorithm, called the longest dynamic critical path (LDCP) algorithm, for HeDCSs. The LDCP algorithm is a list-based scheduling algorithm that uses a new attribute to effectively compute the priorities of tasks in HeDCSs. At each scheduling step, the LDCP algorithm selects the task with the highest priority and assigns the selected task to the processor that minimizes its finish execution time using an insertion-based scheduling policy. The LDCP algorithm successfully generates task schedules that outperform, to the best of our knowledge, two of the best scheduling algorithms for HeDCSs
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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