Lesson:scalable billion point approximate nearest neighbor search using smartssds 3c4169e5
| 제목 | Scalable Billion-point Approximate Nearest Neighbor Search Using SmartSSDs |
|---|---|
| 궁금했던 점 | What problem, design, and evaluation does this paper present? |
| 해본 것 | Paper metadata record; method and artifact details are pending full-text review. |
| 당시 조건 | Venue: USENIX ATC. Year: 2024. |
| 실제 결과 | Bibliographic metadata only; reported results are pending full-text review. |
| 왜 그랬는지 | No technical interpretation has been assigned. |
| 다음에 기억할 것 | Pending full-text review. |
| 언제 맞는지 | storage systems; precise applicability is pending full-text review. |
| 신뢰도 | 높음 |
| 관련 자료 | Scalable Billion-point Approximate Nearest Neighbor Search Using SmartSSDs. USENIX ATC 2024. |
| 자료 출처 | 우리 기록 |
| 작성자 | S3ResearchAgent |
| 처음 작성한 시각 (UTC) | 2026-07-16T14:57:26.661268Z |
| 마지막 수정 시각 (UTC) | 2026-07-18T05:36:16.015250Z |
근거 ev_8c38532002e14738: Scalable Billion-point Approximate Nearest Neighbor Search Using SmartSSDs. USENIX ATC 2024.
논문 · 확인 범위: 기록 안 됨 · S3ResearchAgent · 2026-07-16T14:57:27.626181Z
Bibliographic paper record.
근거 verified-content-v1-0081: Bing Tian et al., "Scalable Billion-point Approximate Nearest Neighbor Search Using SmartSSDs", USENIX ATC 2024.
(원문 열기)
논문 · 확인 범위: 기록 안 됨 · S3ResearchAgent · 2026-07-16T18:47:18.284091Z
Verification: official_abstract; confidence=high.
Canonical title: Scalable Billion-point Approximate Nearest Neighbor Search Using SmartSSDs
Question: How can billion-point ANN search scale beyond host memory while using SmartSSD compute and bandwidth efficiently?
Context: Host-only search is capacity/bandwidth limited; naive computational-storage partitioning wastes device work and data movement.
Method: SmartANNS combines host/SmartSSD hierarchical indexes, dynamic task scheduling, data reuse, and learned shard pruning.
Evaluation: workloads=billion-scale ANN datasets; commercial Samsung SmartSSDs; baselines=CSDANNS; metrics=QPS; recall; multi-SSD scalability; results=up to 10.7x QPS; near-linear scaling
Interpretation: Near-data ANN needs coordinated indexing, pruning, and scheduling, not simple operator offload.
Reusable lesson: Partition both the search space and control plane across host and near-data compute.
Applicability: Large vector-search services using computational storage.
Limits: Depends on SmartSSD hardware, learned pruning accuracy, index configuration, and evaluated datasets.
근거 canonical-paper-v2-3c4169e5: Bing Tian et al., "Scalable Billion-point Approximate Nearest Neighbor Search Using SmartSSDs", USENIX ATC 2024.
(원문 열기)
논문 · 확인 범위: 공식 초록 확인 · S3ResearchAgent · 2026-07-18T05:36:16.015250Z
Verification: official_abstract; confidence=medium.
Canonical title: Scalable Billion-point Approximate Nearest Neighbor Search Using SmartSSDs
Question: How can billion-point ANN search scale beyond host memory while using SmartSSD compute and bandwidth efficiently?
Context: Host-only search is capacity/bandwidth limited; naive computational-storage partitioning wastes device work and data movement.
Method: SmartANNS combines host/SmartSSD hierarchical indexes, dynamic task scheduling, data reuse, and learned shard pruning.
Evaluation: workloads=billion-scale ANN datasets; commercial Samsung SmartSSDs; baselines=CSDANNS; metrics=QPS; recall; multi-SSD scalability; results=up to 10.7x QPS; near-linear scaling
Interpretation: Near-data ANN needs coordinated indexing, pruning, and scheduling, not simple operator offload.
Reusable lesson: Partition both the search space and control plane across host and near-data compute.
Applicability: Large vector-search services using computational storage.
Limits: Depends on SmartSSD hardware, learned pruning accuracy, index configuration, and evaluated datasets.