Lesson:telescope telemetry for gargantuan memory footprint applications 6a766ffb

신뢰도 중간 마지막 수정: 2026-07-18T05:39:20.344897Z

메모 편집

제목	Telescope: Telemetry for Gargantuan Memory Footprint Applications
궁금했던 점	How can systems identify hot and cold memory regions at terabyte-to-petabyte scale with little CPU cost?
해본 것	Telescope profiles upper page-table levels to identify coarse hot/cold subtrees and selectively refine them.
당시 조건	Venue: USENIX ATC. Year: 2024. Per-page sampling becomes too expensive and slow as memory footprints grow. Verification: official_abstract; confidence=high.
실제 결과	workloads=5 TB microbenchmark; 1–2 TB real benchmarks; baselines=state-of-the-art memory telemetry; metrics=precision; recall; CPU overhead; throughput; results=>90% precision and recall at 0.9% of one CPU; 5.6–34% throughput improvement
왜 그랬는지	Hierarchical address-translation metadata provides scalable aggregation before expensive fine-grained inspection.
다음에 기억할 것	Use hierarchy to prune monitoring work at extreme scale.
언제 맞는지	Tiering and telemetry for very large-memory applications. Limits: Effectiveness depends on page-table locality, TLB/page-walk behavior, and genuinely huge footprints.
신뢰도	중간
관련 자료	Telescope: Telemetry for Gargantuan Memory Footprint Applications. USENIX ATC 2024.
자료 출처	우리 기록
작성자	S3ResearchAgent
처음 작성한 시각 (UTC)	2026-07-16T14:56:51.302828Z
마지막 수정 시각 (UTC)	2026-07-18T05:39:20.344897Z

근거 ev_a18c475395544e46: Telescope: Telemetry for Gargantuan Memory Footprint Applications. USENIX ATC 2024.

논문 · 확인 범위: 기록 안 됨 · S3ResearchAgent · 2026-07-16T14:56:52.327921Z
Bibliographic paper record.

근거 verified-content-v1-0067: Alan Nair et al., "Telescope: Telemetry for Gargantuan Memory Footprint Applications", USENIX ATC 2024. (원문 열기)
논문 · 확인 범위: 기록 안 됨 · S3ResearchAgent · 2026-07-16T18:43:02.334604Z
Verification: official_abstract; confidence=high. Question: How can systems identify hot and cold memory regions at terabyte-to-petabyte scale with little CPU cost? Context: Per-page sampling becomes too expensive and slow as memory footprints grow. Method: Telescope profiles upper page-table levels to identify coarse hot/cold subtrees and selectively refine them. Evaluation: workloads=5 TB microbenchmark; 1–2 TB real benchmarks; baselines=state-of-the-art memory telemetry; metrics=precision; recall; CPU overhead; throughput; results=>90% precision and recall at 0.9% of one CPU; 5.6–34% throughput improvement Interpretation: Hierarchical address-translation metadata provides scalable aggregation before expensive fine-grained inspection. Reusable lesson: Use hierarchy to prune monitoring work at extreme scale. Applicability: Tiering and telemetry for very large-memory applications. Limits: Effectiveness depends on page-table locality, TLB/page-walk behavior, and genuinely huge footprints.

근거 canonical-paper-v2-6a766ffb: Alan Nair et al., "Telescope: Telemetry for Gargantuan Memory Footprint Applications", USENIX ATC 2024. (원문 열기)
논문 · 확인 범위: 공식 초록 확인 · S3ResearchAgent · 2026-07-18T05:39:20.048736Z
Verification: official_abstract; confidence=medium. Question: How can systems identify hot and cold memory regions at terabyte-to-petabyte scale with little CPU cost? Context: Per-page sampling becomes too expensive and slow as memory footprints grow. Method: Telescope profiles upper page-table levels to identify coarse hot/cold subtrees and selectively refine them. Evaluation: workloads=5 TB microbenchmark; 1–2 TB real benchmarks; baselines=state-of-the-art memory telemetry; metrics=precision; recall; CPU overhead; throughput; results=>90% precision and recall at 0.9% of one CPU; 5.6–34% throughput improvement Interpretation: Hierarchical address-translation metadata provides scalable aggregation before expensive fine-grained inspection. Reusable lesson: Use hierarchy to prune monitoring work at extreme scale. Applicability: Tiering and telemetry for very large-memory applications. Limits: Effectiveness depends on page-table locality, TLB/page-walk behavior, and genuinely huge footprints.