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\nprocesses. We investigate several new N-process shared-memory algorithms for variants\nof ME, each of which uses only reads and writes, and is local-spin, i.e., has bounded\nremote memory reference (RMR) complexity. We study these algorithms under two\ndifferent shared-memory models: the distributed shared-memory (DSM) model, and the\ncache-coherent (CC) model. In particular, we present the first known algorithm for first-\ncome-first-served (FCFS) ME that has O(log N) RMR complexity in both the DSM and\nCC models, and uses only atomic reads and writes. Our algorithm is also adaptive to\npoint contention, i.e., the number of processes that are simultaneously active during a\npassage by some process. More precisely, the number of RMRs a process makes per\npassage in our algorithm is \\Theta(min(c, log N)), where c is the point contention. We also\npresent the first known FCFS abortable ME algorithm that is local-spin and uses only\natomic reads and writes. This algorithm has O(N) RMR complexity in both the DSM\nand CC models, and is in the form of a transformation from abortable ME to FCFS\nabortable ME. In conjunction with other results, this transformation also yields the\nfirst known local-spin group mutual exclusion algorithm that uses only atomic reads\nand writes. Additionally, we present the first known local-spin k-exclusion algorithms\nthat 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\nof these algorithms satisfies a new fairness property, called k-FCFS, that generalizes the\nFCFS fairness property for ME algorithms. A modification of this algorithm satisfies the\nstronger first-in-first-enabled (FIFE) fairness property. Finally, we present a modification\nto the FIFE k-exclusion algorithm that works with non-atomic reads and writes. The\nhigh-level structure of all our k-exclusion algorithms is inspired by Lamport’s famous\nBakery algorithm.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 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".