Measuring Effective Work to Reward Success in Dynamic Transaction Scheduling
Bibliographic record
Abstract
One of the greatest challenges of modern computing is the development of software optimized for parallel execution in multi-core processors. Transactional Memory (TM) is a new trend in concurrency control that has emerged to address these challenges. TM promises the performance of finer grain locks combined with lower programming complexity. However, transactional memories are speculative and rely on contention managers to resolve conflicts between transactions. This paper explores a complementary approach to boost the performance of TM through the use of schedulers. A TM scheduler is a software component that decides when a particular transaction should be executed. TM scheduling mechanisms are typically restricted to either serialization or yielding. Moreover, their effectiveness is very sensitive to the accuracy of the metric used to predict transaction behavior, particularly in high-contention scenarios. This paper proposes a new Dynamic Transaction Scheduler (DTS) to select a transaction to execute next, based on a new policy that rewards success and uses an improved metric that measures the amount of effective work performed by a transaction. An experimental evaluation indicates that scheduling transactions based on DTS can provide good average-case performance.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".