A Scalable Recoverable Skip List for Persistent Memory
Bibliographic record
Abstract
Interest in recoverable, persistent-memory-resident (PMEM-resident) data structures is growing as availability of Intel Optane Data Center Persistent Memory increases. An interesting use case for inmemory, recoverable data structures is for database indexes, which need high availability and reliability. RECIPE, a popular conversion technique to make existing, proven-correct algorithms recoverable, is limited to certain classes of algorithms and does not prescribe how to reference data stored in relocatable regions of memory. The Untitled Persistent Skip List (UPSkipList) is a PMEM-resident recoverable skip list derived from Herlihy et al.'s lock-free skip list algorithm. It is developed using a new conversion technique that extends the RECIPE algorithm by Lee et al. to work on lock-free algorithms with non-blocking writes and no inherent recovery mechanism. The algorithm is also extended to support concurrent data node splitting to improve performance. Comparison was done against the BzTree of Arulraj et al., as implemented by Lersch et al., which has non-blocking, non-repairing writes implemented using the persistent multi-word CAS (PMwCAS) primitive by Wang et al. Tested with the Yahoo Cloud Serving Benchmark (YCSB), UPSkipList achieves better performance in write-heavy workloads at high levels of concurrency than BzTree, showing that the extension to RECIPE is an effective alternative.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".