Bibliographic record
Abstract
The allocation of lock objects to critical sections in concurrent programs affects both performance and correctness.Traditionally, this allocation is done manually by the programmer.Recent work explores automatic lock allocation, aiming primarily to minimize conflicts and maximize parallelism by allocating locks to individual critical sections.We investigate several modes of lock allocation, using connected components (groups) of interfering critical sections on a critical section interference graph as the basis for allocation decisions.Our allocator uses thread-based side effect analysis which is built from several pluggable component analyses.It benefits from precise points-to and may happen in parallel information.Thread-local object information provides a small improvement over points-to analysis alone.Our framework minimizes the restrictions on input programs, dealing gracefully with nesting and deadlock, and requiring only simple annotations identifying critical sections.Legacy programs using synchronized regions can be processed without alteration.We find that dynamic locks do not broadly improve upon identical allocations of static locks, but allocating several dynamic locks in place of a single static lock can significantly increase parallelism in certain situations.We experiment with a range of small and large Java benchmarks on 1 to 8 processors, and find that a singleton allocation is sufficient for five of our benchmarks, and that a static allocation with Spark points-to analysis is sufficient for another two.Of the other five benchmarks, two require the use of all phases of our analysis, one depends on using the lockset allocation, and two benchmarks proved too complex to be automatically transformed to satisfactory performance.i
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".