On Implementing SWMR Registers from SWSR Registers in Systems with Byzantine Failures
Bibliographic record
Abstract
The implementation of registers from (potentially) weaker registers is a classical problem in the theory of distributed computing. Since Lamport’s pioneering work [Leslie Lamport, 1986], this problem has been extensively studied in the context of asynchronous processes with crash failures. In this paper, we investigate this problem in the context of Byzantine process failures, with and without process signatures. In particular, we first show a strong impossibility result, namely, that there is no wait-free linearizable implementation of a 1-writer n-reader register from atomic 1-writer (n-1)-reader registers. In fact, this impossibility result holds even if all the processes except the writer are given atomic 1-writer n-reader registers, and even if we assume that the writer can only crash and at most one reader is subject to Byzantine failures. In light of this impossibility result, we give two register implementations. The first one implements a 1-writer n-reader register from atomic 1-writer 1-reader registers. This implementation is linearizable (under any combination of Byzantine process failures), but it is wait-free only under the assumption that the writer is correct or no reader is Byzantine - thus matching the impossibility result. The second implementation assumes process signatures; it is wait-free and linearizable under any number and combination of Byzantine process failures.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.016 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".