arXiv (All)
open access
Aker: Density-Aware Approximate Caching for Vector Search (Extended Version)
Disk-based approximate nearest neighbor search (ANNS) incurs high I/O overhead due to frequent disk accesses during index traversal. Approximate caching, which reuses the results of past queries to serve future similar queries, offers a promising approach to bypass expensive disk searches. However,
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Aker: Density-Aware Approximate Caching for Vector Search (Extended Version)#639751
- Low-Latency Out-of-Core ANN Search in High-Dimensional Space#168418
- Effectiveness and predictability of in-network storage cache for Scientific Workflows#1038674
- ScaleGANN: Accelerate Large-Scale ANN Indexing by Cost-effective Cloud GPUs#175358
- Leveraging I/O Stalls for Efficient Scheduling in ANNS#204867
Record · ID 926604
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive