Local-spin algorithms for variants of mutual exclusion using read and write operations
Bibliographic record
Abstract
Mutual exclusion (ME) is used to coordinate access to shared resources by concurrent processes. We investigate several new N-process shared-memory algorithms for variants of ME, each of which uses only reads and writes, and is local-spin, i.e., has bounded remote memory reference (RMR) complexity. We study these algorithms under two different shared-memory models: the distributed shared-memory (DSM) model, and the cache-coherent (CC) model. In particular, we present the first known algorithm for first- come-first-served (FCFS) ME that has O(log N) RMR complexity in both the DSM and CC models, and uses only atomic reads and writes. Our algorithm is also adaptive to point contention, i.e., the number of processes that are simultaneously active during a passage by some process. More precisely, the number of RMRs a process makes per passage in our algorithm is \Theta(min(c, log N)), where c is the point contention. We also present the first known FCFS abortable ME algorithm that is local-spin and uses only atomic reads and writes. This algorithm has O(N) RMR complexity in both the DSM and CC models, and is in the form of a transformation from abortable ME to FCFS abortable ME. In conjunction with other results, this transformation also yields the first known local-spin group mutual exclusion algorithm that uses only atomic reads and writes. Additionally, we present the first known local-spin k-exclusion algorithms that use only atomic reads and writes and tolerate up to k − 1 crash failures. These algorithms have RMR complexity O(N) in both the DSM and CC models. The simplest of these algorithms satisfies a new fairness property, called k-FCFS, that generalizes the FCFS fairness property for ME algorithms. A modification of this algorithm satisfies the stronger first-in-first-enabled (FIFE) fairness property. Finally, we present a modification to the FIFE k-exclusion algorithm that works with non-atomic reads and writes. The high-level structure of all our k-exclusion algorithms is inspired by Lamport’s famous Bakery algorithm.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".