A Distributed Low-Complexity Coding Solution for Large-Scale Distributed FFT
Bibliographic record
Abstract
In distributed computing, a number of available helper nodes assist in completing a task for the master node. In such setups, the failure or straggling of even a single helper node can significantly increase the processing time. Therefore, coded distributed computing has been the subject of many recent studies. A problem that arises in some setups is that the master's decoding complexity may exceed the complexity of self-computation, rending distributed computing useless. One such case is distributed large-scale FFT, where many helper nodes are required. In this work, we propose a novel distributed coded FFT, where the master's load is significantly lower than the existing work. The gain is obtained by (1) using a novel distributed FFT structure which allows for reliable distributed coding at the Shuffle stage, and (2) using Raptor codes which enjoy a linear complexity at the cost of a small number of extra helper nodes. Numerical results are provided to support the benefits of our proposed solution and to optimize design parameters.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".