Locality-Aware Cache Replacement Policy for Graph Traversals
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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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it