Heterogeneous Coded Distributed Computing with Nonuniform Input File Popularity
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Bibliographic record
Abstract
This paper studies the heterogeneous coded distributed computing (CDC) where input files required for job access have nonuniform popularity. We propose a file placement strategy that can handle an arbitrary number of input files and a nested coded shuffling strategy to effectively explore coded multicasting opportunities. We then formulate the joint optimization of the proposed file placement strategy and shuffling design variables into a mixed-integer linear programming (MILP) problem. To reduce the computational complexity, we propose a simple two-file-group-based approach to obtain an approximate solution. Numerical results show that the proposed two-file-group-based approach achieves nearly the same performance as solving the MILP problem using the conventional branch-and-cut method but with substantially lower computational complexity.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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