Lesson:scalable billion point approximate nearest neighbor search using smartssds 3c4169e5
| 제목 | Scalable Billion-point Approximate Nearest Neighbor Search Using SmartSSDs |
|---|---|
| 궁금했던 점 | How can billion-point ANN search scale beyond host memory while using SmartSSD compute and bandwidth efficiently? |
| 해본 것 | SmartANNS combines host/SmartSSD hierarchical indexes, dynamic task scheduling, data reuse, and learned shard pruning. |
| 당시 조건 | Venue: USENIX ATC. Year: 2024.
Host-only search is capacity/bandwidth limited; naive computational-storage partitioning wastes device work and data movement. Verification: official_abstract; confidence=high. |
| 실제 결과 | 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 |
| 왜 그랬는지 | Near-data ANN needs coordinated indexing, pruning, and scheduling, not simple operator offload. |
| 다음에 기억할 것 | Partition both the search space and control plane across host and near-data compute. |
| 언제 맞는지 | Large vector-search services using computational storage.
Limits: Depends on SmartSSD hardware, learned pruning accuracy, index configuration, and evaluated datasets. |
| 신뢰도 | 중간 |
| 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) | 2026-07-18T14:59:01.637789Z |
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.
자료 검증 verify_860c9d8bf7f1e7f05e43:
ev_8c38532002e14738 ·
판단 보류
확인 범위: 일부 자료 확인 · 주장: context · S3ResearchAgent · 2026-07-18T14:59:01.045283Z
자료: R2-RESTIC:7f893ca5afd2cfb6fe320e9b61063ccc70e75a7a96589420038c8cf338b273be; archive-manifest-sha256=e28171fb69e141ce306d92dfe4b10e6cdc6e81d4fa910c30a846204dbcf8edf8; sha256=d796a64c421ff3bc9bf8ce2cf7db5cdd8558b53df9315b83dc4a1c6da3156b02 / 위치: 보존 파일 objects/sha256/d7/d796a64c421ff3bc9bf8ce2cf7db5cdd8558b53df9315b83dc4a1c6da3156b02
보존 원문 객체를 확보했으나 이 일괄 검증에서는 claim-bearing 범위를 재판정하지 않아 결론을 보류함.
자료 검증 verify_69718702197204b7b125:
verified-content-v1-0081 ·
판단 보류
확인 범위: 일부 자료 확인 · 주장: context · S3ResearchAgent · 2026-07-18T14:59:01.475432Z
자료: R2-RESTIC:7f893ca5afd2cfb6fe320e9b61063ccc70e75a7a96589420038c8cf338b273be; archive-manifest-sha256=e28171fb69e141ce306d92dfe4b10e6cdc6e81d4fa910c30a846204dbcf8edf8; sha256=d796a64c421ff3bc9bf8ce2cf7db5cdd8558b53df9315b83dc4a1c6da3156b02 / 위치: 보존 파일 objects/sha256/d7/d796a64c421ff3bc9bf8ce2cf7db5cdd8558b53df9315b83dc4a1c6da3156b02
보존 원문 객체를 확보했으나 이 일괄 검증에서는 claim-bearing 범위를 재판정하지 않아 결론을 보류함.
자료 검증 verify_44427df205daf294b4fb:
canonical-paper-v2-3c4169e5 ·
판단 보류
확인 범위: 일부 자료 확인 · 주장: context · S3ResearchAgent · 2026-07-18T14:59:01.637789Z
자료: R2-RESTIC:7f893ca5afd2cfb6fe320e9b61063ccc70e75a7a96589420038c8cf338b273be; archive-manifest-sha256=e28171fb69e141ce306d92dfe4b10e6cdc6e81d4fa910c30a846204dbcf8edf8; sha256=d796a64c421ff3bc9bf8ce2cf7db5cdd8558b53df9315b83dc4a1c6da3156b02 / 위치: 보존 파일 objects/sha256/d7/d796a64c421ff3bc9bf8ce2cf7db5cdd8558b53df9315b83dc4a1c6da3156b02
보존 원문 객체를 확보했으나 이 일괄 검증에서는 claim-bearing 범위를 재판정하지 않아 결론을 보류함.