Hardware Support for Relaxed Concurrency Control in Transactional Memory
Bibliographic record
Abstract
Today's transactional memory systems implement the two-phase-locking (2PL) algorithm which aborts transactions every time a conflict happens. 2PL is a simple algorithm that provides fast transactional operations. However, it limits concurrency in applications with high contention by increasing the rate of aborts. More relaxed algorithms that can commit conflicting transactions have recently been shown to provide better concurrency both in software and hardware. However, existing approaches for implementing such algorithms increase latencies of transactional operations, require complex hardware support and alter standard cache coherence protocols. In this paper, we discuss how a relaxed concurrency control algorithm can be efficiently implemented in hardware. More specifically, we use a technique which approximates conflict-serializability and implement it in hardware on top a base hardware transactional memory system that provides support for isolation and conflict detection. Our novel hardware scheme is based on recording conflicts as they occur, instead of aborting transactions. Transactions serialize at commit time according to these conflicts by sending broadcast messages. Our evaluation of this hardware scheme using a simulator and standard benchmarks shows that it captures the benefits of conflict-serializability. Applications with long transactions and high contention benefit the most, abort rates are reduced up to 7.2 times and the performance is improved up to 66%. We argue that this improvement comes with little additional hardware complexity and requires no changes to the transactional programming model.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".