Bibliographic record
Abstract
A novel coding scheme is proposed to speed up distributed computation through a form of approximate computing. It is known that task replication can greatly mitigate the “straggler effect” in cloud computing, wherein an overall computation can be significantly delayed by slowed processing nodes (or “stragglers”). It has also been demonstrated that, in certain contexts, ideas of error-correction coding can more efficiently deal with stragglers than pure replication. The approach proposed herein builds on these earlier observations through an “anytime” approach to approximate computing. In this paradigm, over time one can produces approximate solutions of increasing accuracy. To accomplish this we first decompose a computational job in to tasks of various priorities. Next, we apply linear error correction coding to produce subtasks that are assigned to different processors. The decomposition used has a big effect on the type of anytime performance we attain. We study this scheme in a general framework in terms of the expected cost of the approximate solution. We further explore the approach in the context of vector-matrix multiplication. The proposed construction is numerically studied and, in comparison to previous work, demonstrates a significant improvement in the accuracy/latency trade-off.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".