Locality-Aware Cache Replacement Policy for Graph Traversals
Bibliographic record
Abstract
Many graph processing applications consist of read-only workloads that need to perform low-latency traversals over large graphs. These traversals are inherently expensive, and storage and processing systems need to be optimized for them. The performance of secondary storage-based systems can be improved by caching locality-driven data in memory. Exploring the data reuse of graph objects in applications is important to decrease the page faults in the cache. However, graph applications can suffer from poor access locality, making caching of graph data challenging. Locality can be imposed through graph ordering algorithms that can be exploited by cache replacement algorithms. We propose a graph locality-aware cache replacement policy called LAC that exploits the serialization layout obtained by graph ordering techniques. We show that the spatial locality that is captured on disk pages offers temporal locality for subsequent accesses of cache pages, and this information can be used to make improved cache replacement decisions. We evaluate LAC against the popular GCLOCK algorithm for input graphs with different structural properties while running various query types. Our evaluation shows that LAC can outperform GCLOCK through page fault improvements by reducing latency up to 1.42X in simulation studies and up to 1.23X with integration into the Neo4j system.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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 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".