Bibliographic record
Abstract
Fine-grained locking is often necessary to increase concurrency. Correctly implementing fine-grained locking with today's concurrency primitives can be challenging—race conditions often plague programs with sophisticated locking schemes. We present views, a new approach to concurrency control. Views ease the task of implementing sophisticated locking schemes and provide static checks to automatically detect many data races. A view of an object declares a partial interface, consisting of fields and methods, to the object that the view protects. A view also contains an incompatibility declaration, which lists views that may not be simultaneously held by other threads. A set of view annotations specify which code regions hold a view of an object. Our view compiler performs simple static checks that identify many data races. We pair the basic approach with an inference algorithm that can infer view incompatibility specifications for many applications. We have ported four benchmark applications to use views: portions of Vuze, a BitTorrent client; Mailpuccino, a graphical email client; jphonelite, a VoIP softphone implementation; and TupleSoup, a database. Our experience indicates that views are easy to use, make implementing sophisticated locking schemes simple, and can help eliminate concurrency bugs. We have evaluated the performance of a view implementation of a red-black tree and found that views can significantly improve performance over that of the lock-based implementation.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.179 | 0.107 |
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".