Speculative Versioning through Perceptron Predictors
Bibliographic record
Abstract
A well-know method to avoid inconsistent state in Software Transactional Memory (STM) is a globally shared version clock whose values are used to tag memory locations. While this method does not require frequent validation of transactional data, it results in contentions over the global clock. Each time that a transaction commits it updates the global clock which results in costly coherence misses. The alternative approach is local clock which requires access to local variables instead of a global version clock. However, as we show in this paper, the optimum validation policy changes not only across applications but also within an application and through different phases of a program. To counter this challenge, we introduce Speculative Versioning (SV) which dynamically selects one of the two validation techniques based on probability of conflicts. SV is a speculative approach and relies on perceptron predictors to predict future conflicts. We have incorporated SV into TL2 and compared the performance of the new implementation with the original STM using Stamp v0.9.10 benchmark suite. Our results reveal that SV is effective and improves execution time of transactional applications up to 31%.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".