Read-Write Lock Allocation in Software Transactional Memory
Bibliographic record
Abstract
Transactional Memory (TM) is a promising programming model for managing concurrent accesses to the shared memory locations. Time-based Software Transactional Memories (STMs) exploit a global clock to maintain consistency of transactions and validate transactional data. One of the shortcomings of this technique is that the global clock becomes bottleneck as the number of transactions increases. In this paper, we introduce two optimization techniques to overcome the overhead of the global clock. The first technique is Read-Write Lock Allocation (RWLA) which does not exploit any central data structure to maintain consistency of transactions. This method improves performance of STMs only if transactions commit successfully. However, in the event of frequent conflicts, RWLA increases cost of abort and degrades performance. Our second optimization technique is an adaptive technique which dynamically selects either baseline scheme or RWLA. Our experimental results reveal that our adaptive technique is effective and is able to improve performance of transactional applications up to 66%.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".