Lesson:telescope telemetry for gargantuan memory footprint applications 6a766ffb
| 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.