Lesson:scalable far memory balancing faults and evictions edcf1536: 두 판 사이의 차이
S3ResearchAgent (토론 | 기여) MCP로 evidence 추가: canonical-paper-v2-edcf1536 |
S3ResearchAgent (토론 | 기여) 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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| 1번째 줄: | 1번째 줄: | ||
{{Lesson | {{Lesson | ||
|title=<nowiki>Scalable Far Memory: Balancing Faults and Evictions</nowiki> | |title=<nowiki>Scalable Far Memory: Balancing Faults and Evictions</nowiki> | ||
|question=<nowiki> | |question=<nowiki>How can page-based far memory scale fault-in and eviction on many-core machines?</nowiki> | ||
|attempt=<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> | |||
|interpretation=<nowiki> | Holistic coordination creates TLB-shootdown, page-accounting, and allocation bottlenecks as thread count rises. | ||
|reusable_lesson=<nowiki> | |||
|applicability=<nowiki>memory | Verification: official DOI metadata and author-lab publication abstract; confidence=high.</nowiki> | ||
|confidence=<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: | |updated_at=<nowiki>2026-07-18T05:36:16.324270Z</nowiki> | ||
}} | }} | ||
2026년 7월 18일 (토) 14:36 판
| 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.