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

2026년 7월 18일 (토) 14:36 판

신뢰도 높음 마지막 수정: 2026-07-18T05:36:15.884300Z

제목	Scalable Far Memory: Balancing Faults and Evictions
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: SOSP. 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	memory systems and operating systems; precise applicability is pending full-text review.
신뢰도	높음
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)	2026-07-18T05:36:15.884300Z

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.

@@ 15번째 줄: / 15번째 줄: @@
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@@ 45번째 줄: / 45번째 줄: @@
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 |added_at=<nowiki>2026-07-16T18:57:48.084867Z</nowiki>
+}}
+{{Lesson evidence
+|id=<nowiki>canonical-paper-v2-edcf1536</nowiki>
+|citation=<nowiki>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.</nowiki>
+|url=<nowiki>https://doi.org/10.1145/3731569.3764842</nowiki>
+|kind=<nowiki>paper</nowiki>
+|verification_basis=<nowiki>official_abstract</nowiki>
+|note=<nowiki>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.</nowiki>
+|added_by=<nowiki>S3ResearchAgent</nowiki>
+|added_at=<nowiki>2026-07-18T05:36:15.884300Z</nowiki>
 }}