Making lockless synchronization fast: performance implications of memory reclamation
Bibliographic record
Abstract
Achieving high performance for concurrent applications on modern multiprocessors remains challenging. Many pro-grammers avoid locking to improve performance, while oth-ers replace locks with non-blocking synchronization to pro-tect against deadlock, priority inversion, and convoying. In both cases, dynamic data structures that avoid locking, re-quire a memory reclamation scheme that reclaims nodes once they are no longer in use. The performance of existing memory reclamation schemes has not been thoroughly evaluated. We conduct the first fair and comprehensive comparison of three recent schemes—quiescent-state-based reclamation, epoch-based reclamation, and hazard-pointer-based reclamation—using a flexible microbenchmark. Our results show that there is no globally optimal scheme. When evaluating lockless syn-chronization, programmers and algorithm designers should thus carefully consider the data structure, the workload, and the execution environment, each of which can dramati-cally affect memory reclamation performance. 1
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".