Relaxing concurrency control in transactional memory
Bibliographic record
Abstract
Transactional memory (TM) systems have gained considerable popularity in the last decade driven by the increased demand for tools that ease parallel programming. TM eliminates the need for user-locks that protect accesses to shared data. It offers performance close to that of fine-grain locking with the programming simplicity of coarse-grain locking. Today's TM systems implement the two-phase-locking (2PL) algorithm which aborts transactions every time a conflict occurs. 2PL is a simple algorithm that provides fast transactional operations. However, it limits concurrency in applications with high contention because it increases the rate of aborts. We propose the use of a more relaxed concurrency control algorithm to provide better concurrency. This algorithm is based on the conflict-serializability (CS) model. Unlike 2PL, it allows some transactions to commit successfully even when they make conflicting accesses. We implement this algorithm both in a software TM system as well as in a simulator of a hardware TM system. Our evaluation using TM benchmarks shows that the algorithm improves the performance of applications with long transactions and high abort rates. Performance is improved by up to 299% in the software TM, and up to 66% in the hardware simulator. We argue that these improvements come with little additional complexity and require no changes to the transactional programming model. This makes our implementation feasible.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".