First-Come-First-Served as a Separate Principle
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A lock is a mechanism to guarantee mutual exclusion with eventual progress, i.e., some degree of fairness. First-come-first-served (FCFS) progress is perfectly fair. FCFS progress can be offered by a locking algorithm or added by wrapping a non-FCFS lock with a separate FCFS algorithm. A new separate FCFS algorithm is presented that creates FCFS progress by wrapping a lock’s entry protocol (acquire). The algorithm addresses an important safety issue in locking, called barging, where arriving threads proceed before waiting threads or waiting threads are serviced with some bias. Barging increases latency for waiting threads and is non-intuitive to concurrent programmers, even though it is inherent to non-deterministic concurrent execution. A correctness proof is presented for the new FCFS algorithm and verified with the proof assistant PVS. Experimental tests are performed to ensure the presented wrapper provides FCFS progress when used to transform non-FCFS software and hardware locks. The performance of the non-FCFS and transformed FCFS counterparts is compared and contrasted with locks using inherent FCFS progress. The results show the FCFS transforms are performant for most algorithms, providing an additional tool for application developers to achieve correctness without a significant global performance reduction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it