Deadline-Aware Coded Computation Across Homogeneous Workers
Why this work is in the frame
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Bibliographic record
Abstract
Distributed computing systems have been widely used in recent years to handle massive computations required by newly emerged machine learning algorithms and signal processing problems. In practice, a distributed computing system often receives multiple tasks each needs to be finished by a specific deadline. This necessitates use of a task scheduler which orders and prioritizes tasks executions. In this work, we consider task scheduling for a homogeneous distributed computing system with multiple matrix-vector multiplication jobs, and try to maximize the number of tasks completed before their deadlines. The main challenges in such a system are random task arrivals and random execution times due to the straggling effect. To address these challenges, we propose two task scheduling algorithms namely “simple greedy” and “farsighted greedy” and compare their performance with the ultimate upper bound, i.e., a genie-aided algorithm that knows the exact arrival and execution times of all tasks. Our simulation results demonstrate that the proposed algorithms can approach the performance of the genie-aided algorithm.
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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 | 0.000 | 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 | 0.001 |
| 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