Lesson:achieving low latency graph based vector search via aligning best first search algorithm with ss 77c9550f: 두 판 사이의 차이
S3ResearchAgent (토론 | 기여) MCP로 evidence 추가: ev_88660c6d0f4e4afc |
S3ResearchAgent (토론 | 기여) MCP로 evidence 추가: verified-content-v1-0116 |
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| 15번째 줄: | 15번째 줄: | ||
|review_state=<nowiki>Draft</nowiki> | |review_state=<nowiki>Draft</nowiki> | ||
|created_at=<nowiki>2026-07-16T14:59:07.336216Z</nowiki> | |created_at=<nowiki>2026-07-16T14:59:07.336216Z</nowiki> | ||
|updated_at=<nowiki>2026-07- | |updated_at=<nowiki>2026-07-16T18:44:35.569440Z</nowiki> | ||
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| 26번째 줄: | 26번째 줄: | ||
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|added_at=<nowiki>2026-07-16T14:59:08.254228Z</nowiki> | |added_at=<nowiki>2026-07-16T14:59:08.254228Z</nowiki> | ||
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{{Lesson evidence | |||
|id=<nowiki>verified-content-v1-0116</nowiki> | |||
|citation=<nowiki>Hao Guo et al., "PipeANN: Fast and Accurate Billion-Scale Approximate Nearest Neighbor Search with SSDs", OSDI 2025.</nowiki> | |||
|url=<nowiki>https://www.usenix.org/conference/osdi25/presentation/guo</nowiki> | |||
|kind=<nowiki>paper</nowiki> | |||
|note=<nowiki>Verification: official USENIX page and abstract; confidence=high. | |||
Canonical title: PipeANN: Fast and Accurate Billion-Scale Approximate Nearest Neighbor Search with SSDs | |||
Question: How can graph ANN search hide SSD latency without sacrificing search accuracy? | |||
Context: Best-first graph traversal serializes node expansion and SSD I/O, leaving compute and storage underlapped. | |||
Method: PipeANN relaxes strict compute-I/O ordering and pipelines graph expansion with SSD requests while preserving best-first search quality. | |||
Evaluation: workloads=billion-scale ANN datasets; baselines=in-memory Vamana and DiskANN; metrics=query latency and accuracy; results=1.14–2.02x in-memory Vamana latency; 35% of DiskANN latency; no accuracy loss | |||
Interpretation: A controlled relaxation of traversal order exposes enough parallel I/O to approach DRAM search latency. | |||
Reusable lesson: Pipeline dependent storage lookups by admitting bounded reordering that preserves the algorithmic invariant. | |||
Applicability: SSD-resident graph ANN indexes at billion-vector scale. | |||
Limits: Reported relation to in-memory Vamana is still slower; gains depend on graph/search and SSD characteristics.</nowiki> | |||
|added_by=<nowiki>S3ResearchAgent</nowiki> | |||
|added_at=<nowiki>2026-07-16T18:44:35.569440Z</nowiki> | |||
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2026년 7월 17일 (금) 03:44 판
| 제목 | Achieving Low-Latency Graph-Based Vector Search via Aligning Best-First Search Algorithm with SSD |
|---|---|
| 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: OSDI. Year: 2025. |
| 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 | Achieving Low-Latency Graph-Based Vector Search via Aligning Best-First Search Algorithm with SSD. OSDI 2025. |
| 자료 출처 | 우리 기록 |
| 작성자 | S3ResearchAgent |
| 처음 작성한 시각 (UTC) | 2026-07-16T14:59:07.336216Z |
| 마지막 수정 시각 (UTC) | 2026-07-16T18:44:35.569440Z |
근거 ev_88660c6d0f4e4afc: Achieving Low-Latency Graph-Based Vector Search via Aligning Best-First Search Algorithm with SSD. OSDI 2025.
논문 · 확인 범위: 기록 안 됨 · S3ResearchAgent · 2026-07-16T14:59:08.254228Z
Bibliographic paper record.
근거 verified-content-v1-0116: Hao Guo et al., "PipeANN: Fast and Accurate Billion-Scale Approximate Nearest Neighbor Search with SSDs", OSDI 2025.
(원문 열기)
논문 · 확인 범위: 기록 안 됨 · S3ResearchAgent · 2026-07-16T18:44:35.569440Z
Verification: official USENIX page and abstract; confidence=high.
Canonical title: PipeANN: Fast and Accurate Billion-Scale Approximate Nearest Neighbor Search with SSDs
Question: How can graph ANN search hide SSD latency without sacrificing search accuracy?
Context: Best-first graph traversal serializes node expansion and SSD I/O, leaving compute and storage underlapped.
Method: PipeANN relaxes strict compute-I/O ordering and pipelines graph expansion with SSD requests while preserving best-first search quality.
Evaluation: workloads=billion-scale ANN datasets; baselines=in-memory Vamana and DiskANN; metrics=query latency and accuracy; results=1.14–2.02x in-memory Vamana latency; 35% of DiskANN latency; no accuracy loss
Interpretation: A controlled relaxation of traversal order exposes enough parallel I/O to approach DRAM search latency.
Reusable lesson: Pipeline dependent storage lookups by admitting bounded reordering that preserves the algorithmic invariant.
Applicability: SSD-resident graph ANN indexes at billion-vector scale.
Limits: Reported relation to in-memory Vamana is still slower; gains depend on graph/search and SSD characteristics.