A distributed implementation of sequential consistency with multi-object operations
Bibliographic record
Abstract
Sequential consistency is a consistency criterion for concurrent objects stating that the execution of a multiprocess program is correct if it could have been produced by executing the program on a mono-processor system, preserving the order of the operations of each individual process. Several protocols implementing sequential consistency on top of asynchronous distributed systems have been proposed. They assume that the processes access the shared objects through basic read and write operations. We consider the case where the processes can invoke multiobject operations which can read or write several objects in a single operation atomically. It proposes a particularly simple protocol that guarantees sequentially consistent executions in such a context. The previous sequential consistency protocols, in addition to considering only unary operations, assume either full replication or a central manager storing copies of all the objects. In contrast, the proposed protocol has the noteworthy feature that each object has a separate manager. Interestingly, this provides the protocol with a versatility dimension that allows deriving simple protocols providing sequential consistency or atomic consistency when each operation is on a single object.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".