DULL: A Fast Scalable Detectable Unrolled Lock-Based Linked List
Bibliographic record
Abstract
Persistent memory (PM) has emerged as a promising technology that enables data structures to preserve their consistent state after recovering from system failures. Detectable data structures have been proposed to detect the response of the last operation of a crashed process. Various lock-free detectable and recoverable concurrent data structures have been developed in the literature. However, designing detectable lock-based structures is challenging due to the need to preserve the correctness properties of the underlying locks, such as mutual exclusion and deadlock-freedom, across failures. Therefore, lock-based detectable and persistent data structures are not as common as lock-free structures. In this work, we introduce DULL: a fast, scalable and Detectable Unrolled Lock-based Linked list. This paper presents the design and implementation of DULL, along with an evaluation of its recoverability and scalability. Experimental Results show that DULL is several-fold faster than the competition in all workloads that involve updates. Moreover, as opposed to some of the previous works, our algorithm is scalable when the multiprocessor is oversubscribed. DULL is a demonstration of the feasibility of using lock-based data structures with detectability in PM environments. We believe that DULL opens up new research directions for designing and analyzing detectable lock-based data structures.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".