Group-Wise Coding for Coded Distributed Computing Systems with Group Heterogeneity and Communication Delay
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Bibliographic record
Abstract
In distributed computing, coding techniques are shown to be an effective solution for mitigating the impact of stragglers. However, previous research on coding has predominantly focused on homogeneous worker environments, overlooking the fact that real-world systems often consist of heterogeneous workers with varying computing and communication capabilities. Specifically, uniform load allocation, without considering worker heterogeneity, can result in substantial latency losses. In this paper, we propose a load allocation strategy designed for distributed systems with group heterogeneity, where workers in the same group have similar computing and communication capabilities, but workers of different groups do not. By exploring group-wise MDS codes, we determine the optimal code dimension and optimal computation loads for individual groups. Our proposed approach demonstrates comparable computation times to existing methods, while exhibiting the advantage of much shorter encoding and decoding times.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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