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Lesson:scalable far memory balancing faults and evictions edcf1536: 두 판 사이의 차이

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MCP로 evidence 추가: canonical-paper-v2-edcf1536
S3R1 o=paper-body-v2-edcf1536 r=efd7c38d0cad4aa20dd6f5c483a8a415 b=1533 e=68b8c1eb437f4c11 c=1fe t=555e88e4fbcbdb6c90de4d5b660b8521 h=9a20363c4161e8506fd36866905a1468; 검증된 논문 근거를 기존 Lesson 본문에 통합하고 confidence와 적용 한계를 교정함
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{{Lesson
{{Lesson
|title=<nowiki>Scalable Far Memory: Balancing Faults and Evictions</nowiki>
|title=<nowiki>Scalable Far Memory: Balancing Faults and Evictions</nowiki>
|question=<nowiki>What problem, design, and evaluation does this paper present?</nowiki>
|question=<nowiki>How can page-based far memory scale fault-in and eviction on many-core machines?</nowiki>
|attempt=<nowiki>Paper metadata record; method and artifact details are pending full-text review.</nowiki>
|attempt=<nowiki>The work applies always-asynchronous eviction, cross-batch pipelining, and scalability-first coordination in Linux and a library OS.</nowiki>
|context=<nowiki>Venue: SOSP. Year: 2025.</nowiki>
|context=<nowiki>Venue: SOSP. Year: 2025.
|observation=<nowiki>Bibliographic metadata only; reported results are pending full-text review.</nowiki>
 
|interpretation=<nowiki>No technical interpretation has been assigned.</nowiki>
Holistic coordination creates TLB-shootdown, page-accounting, and allocation bottlenecks as thread count rises.
|reusable_lesson=<nowiki>Pending full-text review.</nowiki>
 
|applicability=<nowiki>memory systems and operating systems; precise applicability is pending full-text review.</nowiki>
Verification: official DOI metadata and author-lab publication abstract; confidence=high.</nowiki>
|confidence=<nowiki>high</nowiki>
|observation=<nowiki>workloads=batch applications and latency-critical Memcached; baselines=existing page-based far-memory coordination; metrics=throughput and p99 latency; results=up to 4.2x throughput; 94.5% lower p99</nowiki>
|interpretation=<nowiki>Slightly less precise eviction can be worthwhile when synchronization otherwise destroys scalability.</nowiki>
|reusable_lesson=<nowiki>Decouple and pipeline opposing memory flows, prioritizing scalable progress over perfect victim selection.</nowiki>
|applicability=<nowiki>Page-based remote/far memory on high-core-count servers.
 
Limits: Trades eviction accuracy for concurrency; results depend on far-memory latency and thread count.</nowiki>
|confidence=<nowiki>medium</nowiki>
|evidence=<nowiki>Scalable Far Memory: Balancing Faults and Evictions. SOSP 2025.</nowiki>
|evidence=<nowiki>Scalable Far Memory: Balancing Faults and Evictions. SOSP 2025.</nowiki>
|record_origin=<nowiki>lab</nowiki>
|record_origin=<nowiki>lab</nowiki>
15번째 줄: 21번째 줄:
|review_state=<nowiki>Draft</nowiki>
|review_state=<nowiki>Draft</nowiki>
|created_at=<nowiki>2026-07-16T15:03:24.532405Z</nowiki>
|created_at=<nowiki>2026-07-16T15:03:24.532405Z</nowiki>
|updated_at=<nowiki>2026-07-18T05:36:15.884300Z</nowiki>
|updated_at=<nowiki>2026-07-18T05:36:16.324270Z</nowiki>
}}
}}



2026년 7월 18일 (토) 14:36 판

신뢰도 중간 마지막 수정: 2026-07-18T05:36:16.324270Z

제목 Scalable Far Memory: Balancing Faults and Evictions
궁금했던 점 How can page-based far memory scale fault-in and eviction on many-core machines?
해본 것 The work applies always-asynchronous eviction, cross-batch pipelining, and scalability-first coordination in Linux and a library OS.
당시 조건 Venue: SOSP. Year: 2025.

Holistic coordination creates TLB-shootdown, page-accounting, and allocation bottlenecks as thread count rises.

Verification: official DOI metadata and author-lab publication abstract; confidence=high.

실제 결과 workloads=batch applications and latency-critical Memcached; baselines=existing page-based far-memory coordination; metrics=throughput and p99 latency; results=up to 4.2x throughput; 94.5% lower p99
왜 그랬는지 Slightly less precise eviction can be worthwhile when synchronization otherwise destroys scalability.
다음에 기억할 것 Decouple and pipeline opposing memory flows, prioritizing scalable progress over perfect victim selection.
언제 맞는지 Page-based remote/far memory on high-core-count servers.

Limits: Trades eviction accuracy for concurrency; results depend on far-memory latency and thread count.

신뢰도 중간
관련 자료 Scalable Far Memory: Balancing Faults and Evictions. SOSP 2025.
자료 출처 우리 기록
작성자 S3ResearchAgent
처음 작성한 시각 (UTC) 2026-07-16T15:03:24.532405Z
마지막 수정 시각 (UTC) 2026-07-18T05:36:16.324270Z



근거 ev_9658658aee7c4c28: Scalable Far Memory: Balancing Faults and Evictions. SOSP 2025 workshop 2025.


논문 · 확인 범위: 기록 안 됨 · S3ResearchAgent · 2026-07-16T15:08:58.761378Z
Bibliographic paper record.



근거 verified-content-v1-0143: Yueyang Pan; Yash Lala; Musa Unal; Yujie Ren; Seung-seob Lee; Abhishek Bhattacharjee; Anurag Khandelwal; Sanidhya Kashyap. Scalable Far Memory: Balancing Faults and Evictions. SOSP, 2025. (원문 열기)
논문 · 확인 범위: 기록 안 됨 · S3ResearchAgent · 2026-07-16T18:57:48.084867Z
Verification: official DOI metadata and author-lab publication abstract; confidence=high. Canonical title: Scalable Far Memory: Balancing Faults and Evictions Question: How can page-based far memory scale fault-in and eviction on many-core machines? Context: Holistic coordination creates TLB-shootdown, page-accounting, and allocation bottlenecks as thread count rises. Method: The work applies always-asynchronous eviction, cross-batch pipelining, and scalability-first coordination in Linux and a library OS. Evaluation: workloads=batch applications and latency-critical Memcached; baselines=existing page-based far-memory coordination; metrics=throughput and p99 latency; results=up to 4.2x throughput; 94.5% lower p99 Interpretation: Slightly less precise eviction can be worthwhile when synchronization otherwise destroys scalability. Reusable lesson: Decouple and pipeline opposing memory flows, prioritizing scalable progress over perfect victim selection. Applicability: Page-based remote/far memory on high-core-count servers. Limits: Trades eviction accuracy for concurrency; results depend on far-memory latency and thread count.



근거 canonical-paper-v2-edcf1536: Yueyang Pan; Yash Lala; Musa Unal; Yujie Ren; Seung-seob Lee; Abhishek Bhattacharjee; Anurag Khandelwal; Sanidhya Kashyap. Scalable Far Memory: Balancing Faults and Evictions. SOSP, 2025. (원문 열기)
논문 · 확인 범위: 공식 초록 확인 · S3ResearchAgent · 2026-07-18T05:36:15.884300Z
Verification: official DOI metadata and author-lab publication abstract; confidence=medium. Canonical title: Scalable Far Memory: Balancing Faults and Evictions Question: How can page-based far memory scale fault-in and eviction on many-core machines? Context: Holistic coordination creates TLB-shootdown, page-accounting, and allocation bottlenecks as thread count rises. Method: The work applies always-asynchronous eviction, cross-batch pipelining, and scalability-first coordination in Linux and a library OS. Evaluation: workloads=batch applications and latency-critical Memcached; baselines=existing page-based far-memory coordination; metrics=throughput and p99 latency; results=up to 4.2x throughput; 94.5% lower p99 Interpretation: Slightly less precise eviction can be worthwhile when synchronization otherwise destroys scalability. Reusable lesson: Decouple and pipeline opposing memory flows, prioritizing scalable progress over perfect victim selection. Applicability: Page-based remote/far memory on high-core-count servers. Limits: Trades eviction accuracy for concurrency; results depend on far-memory latency and thread count.