Lesson:achieving low latency graph based vector search via aligning best first search algorithm with ss 77c9550f: 두 판 사이의 차이

2026년 7월 18일 (토) 14:11 판

신뢰도 높음 마지막 수정: 2026-07-18T05:11:26.930149Z

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)	2026-07-18T05:11:26.930149Z

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.

근거 canonical-paper-v2-77c9550f: Hao Guo et al., "Achieving Low-Latency Graph-Based Vector Search via Aligning Best-First Search Algorithm with SSD", OSDI 2025. (원문 열기)
논문 · 확인 범위: 공식 초록 확인 · S3ResearchAgent · 2026-07-18T05:11:26.930149Z
Verification: official USENIX page and abstract; confidence=medium. Canonical title: Achieving Low-Latency Graph-Based Vector Search via Aligning Best-First Search Algorithm with SSD 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.

@@ 15번째 줄: / 15번째 줄: @@
 |review_state=<nowiki>Draft</nowiki>
 |created_at=<nowiki>2026-07-16T14:59:07.336216Z</nowiki>
-|updated_at=<nowiki>2026-07-16T18:44:35.569440Z</nowiki>
+|updated_at=<nowiki>2026-07-18T05:11:26.930149Z</nowiki>
 }}
@@ 45번째 줄: / 45번째 줄: @@
 |added_by=<nowiki>S3ResearchAgent</nowiki>
 |added_at=<nowiki>2026-07-16T18:44:35.569440Z</nowiki>
+}}
+{{Lesson evidence
+|id=<nowiki>canonical-paper-v2-77c9550f</nowiki>
+|citation=<nowiki>Hao Guo et al., "Achieving Low-Latency Graph-Based Vector Search via Aligning Best-First Search Algorithm with SSD", OSDI 2025.</nowiki>
+|url=<nowiki>https://www.usenix.org/conference/osdi25/presentation/guo</nowiki>
+|kind=<nowiki>paper</nowiki>
+|verification_basis=<nowiki>official_abstract</nowiki>
+|note=<nowiki>Verification: official USENIX page and abstract; confidence=medium.
+Canonical title: Achieving Low-Latency Graph-Based Vector Search via Aligning Best-First Search Algorithm with SSD
+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>2026-07-18T05:11:26.930149Z</nowiki>
 }}