FlashANNS: GPU-Driven Asynchronous I/O Pipelining for Eliminating Storage-Compute Bottlenecks in Billion-Scale Similarity Search
Summary: GPU-driven out-of-core graph ANNS that breaks the SSD/compute bottleneck via dependency-relaxed async pipelining. Query-grained lock-free SSD concurrency plus compute-I/O balanced graph degree selection yield 2.7–12.2x higher throughput at ≥95% recall over DiskANN/SPANN/FusionANNS. (summarized by gpt-5-mini on Apr 11 2026)
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Authors
- 1. Yang Xiao
- 2. Mo Sun
- 3. Ziyu Song
- 4. Bing Tian
- 5. Jie Sun
- 6. Jie Zhang
- 7. Zeke Wang
- 8. Zonghui Wang
- 9. Wenzhi Chen
- 10. Fei Wu
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Outgoing Citations (Sorted by Pagerank)
Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 34 | Similarity Search in High Dimensions via Hashing | 1999 | VLDB | 0.00076637636 |
| 495 | Milvus: A Purpose-Built Vector Data Management System | 2021 | SIGMOD | 0.00021767688 |
| 2,067 | HippogriffDB: Balancing I/O and GPU Bandwidth in Big Data Analytics | 2016 | VLDB | 9.6392739e-05 |
| 3,736 | What Modern NVMe Storage Can Do, And How To Exploit It: High-Performance I/O for High-Performance Storage Engines | 2023 | VLDB | 6.8057888e-05 |
| 4,544 | ScaleStore: A Fast and Cost-Efficient Storage Engine using DRAM, NVMe, and RDMA | 2022 | SIGMOD | 6.1000636e-05 |
| 5,374 | Rethinking Logging, Checkpoints, and Recovery for High-Performance Storage Engines | 2020 | SIGMOD | 5.5424901e-05 |
| 6,184 | Dotori: A Key-Value SSD Based KV Store | 2023 | VLDB | 5.1666338e-05 |
| 7,329 | LRU-C: Parallelizing Database I/Os for Flash SSDs | 2023 | VLDB | 4.7610574e-05 |
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