Scheduling support for transactional memory contention management
Bibliographic record
Abstract
Transactional Memory (TM) is considered as one of the most promising paradigms for developing concurrent applications. TM has been shown to scale well on >multiple cores when the data access pattern behaves "well," i.e., when few conflicts are induced. In contrast, data patterns with frequent write sharing, with long transactions, or when many threads contend for a smaller number of cores, result in numerous conflicts. Until recently, TM implementations had little control of transactional threads, which remained under the supervision of the kernel's transaction-ignorant scheduler. Conflicts are thus traditionally resolved by consulting an STM-level contention manager . Consequently, the contention managers of these "conventional" TM implementations suffer from a lack of precision and often fail to ensure reasonable performance in high-contention workloads. Recently, scheduling-based TM contention-management has been proposed for increasing TM efficiency under high-contention [2, 5, 19]. However, only user-level schedulers have been considered. In this work, we propose, implement and evaluate several novel kernel-level scheduling support mechanisms for TM contention management. We also investigate different strategies for efficient communication between the kernel and the user-level TM library. To the best of our knowledge, our work is the first to investigate kernel-level support for TM contention management. We have introduced kernel-level TM scheduling support into both the Linux and Solaris kernels. Our experimental evaluation demonstrates that lightweight kernel-level scheduling support significantly reduces the number of aborts while improving transaction throughput on various workloads.
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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".