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Paper Citation Record · LEDGER

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster

As of 14 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2605.24461.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2605.24461 v2

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-30T12:30:57.065247Z

measured 52 of 52 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

52 of 52 outbound references displayed

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  • unresolved3
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 72c905c2-b5e6-4cab-a737-25334a5428ce · outbound

This paper cites an unresolved cited work.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster Unresolved cited work

Reference 1

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Observation e2339dcb-4268-4ef2-a26e-20b43aa5c974 · outbound

This paper cites Processor state control for your EC2 instance, April 2024.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster Processor state control for your EC2 instance, April 2024

Reference 2

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Observation de8c3102-aa1e-4485-8a9d-7ed7a073fc9f · outbound

This paper cites AMD and OpenAI Announce Strate- gic Partnership to Deploy 6 Gigawatts of AMD GPUs, 2025.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster AMD and OpenAI Announce Strate- gic Partnership to Deploy 6 Gigawatts of AMD GPUs, 2025

Reference 3

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Observation 4874f013-3028-4bda-984a-d2e9bbf12e85 · outbound

This paper cites A taxonomy and survey of energy- efficient data centers and cloud computing systems.Ad- vances in computers, 82:47–111, 2011.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster A taxonomy and survey of energy- efficient data centers and cloud computing systems.Ad- vances in computers, 82:47–111, 2011

Reference 4

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Observation e0ffb533-edc1-4c3d-98be-6c16d8b69de1 · outbound

This paper cites A survey of design techniques for system- level dynamic power management.IEEE transactions on very large scale integration (VLSI) systems, 8(3):299– 316, 2002.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster A survey of design techniques for system- level dynamic power management.IEEE transactions on very large scale integration (VLSI) systems, 8(3):299– 316, 2002

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 8fddd231-9c29-45b8-a595-bf8811bc0cfb · outbound

This paper cites Bhattacharya, David Culler, Aman Kansal, Sri- ram Govindan, and Sriram Sankar.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster Bhattacharya, David Culler, Aman Kansal, Sri- ram Govindan, and Sriram Sankar

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation e22a4dfc-0fb0-41d8-935c-5f7fd0cf97fe · outbound

This paper cites Power Stabilization for AI Training Datacenters.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster Power Stabilization for AI Training Datacenters

Reference 7

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Source-reported events for the cited work

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Observation 87089e95-0a93-49ec-bc46-a2845c3741c3 · outbound

This paper cites Scaling llama 3 training with efficient parallelism strategies.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster Scaling llama 3 training with efficient parallelism strategies

Reference 8

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Source-reported events for the cited work

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Observation f8ff5e1b-46cd-4f5c-a511-efcd86df944a · outbound

This paper cites Data center energy consumption modeling: A survey.IEEE Communications surveys & tutorials, 18(1):732–794, 2015.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster Data center energy consumption modeling: A survey.IEEE Communications surveys & tutorials, 18(1):732–794, 2015

Reference 9

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Observation 3ae42602-baef-4bd6-8e4e-a3ca2f131980 · outbound

This paper cites Electricity explained: Electricity generation, capacity, and sales in the United States, 2024.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster Electricity explained: Electricity generation, capacity, and sales in the United States, 2024

Reference 10

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Observation 91538666-413e-426f-9c9b-591dcb4967c5 · outbound

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Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster Unresolved cited work

Reference 11

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Source-reported events for the cited work

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Observation 3aaebe45-5b41-4b6a-95a5-b2d5ddee39d9 · outbound

This paper cites an unresolved cited work.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster Unresolved cited work

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 7463e82f-defa-4c71-b46e-3e47675917ca · outbound

This paper cites Unified architecture - opc foundation.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster Unified architecture - opc foundation

Reference 13

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Source-reported events for the cited work

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Observation 6eb803ed-f26c-44d0-af73-f6e36b751e17 · outbound

This paper cites How much power oversubscription is safe and allowed in data cen- ters? InProceedings of the 8th International Conference on Autonomic Computing (ICAC 2011), pages 91–100.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster How much power oversubscription is safe and allowed in data cen- ters? InProceedings of the 8th International Conference on Autonomic Computing (ICAC 2011), pages 91–100

Reference 14

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Observation 5475ab8d-e404-4c7e-b371-d3185f2fbd64 · outbound

This paper cites CPU platforms, April 2024.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster CPU platforms, April 2024

Reference 15

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Source-reported events for the cited work

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Observation 6948e6ec-8889-445c-99fc-ef6f85832a07 · outbound

This paper cites Statisti- cal profiling-based techniques for effective power provi- sioning in data centers.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster Statisti- cal profiling-based techniques for effective power provi- sioning in data centers

Reference 16

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Observation 84d2388b-bb5a-4b7b-9bec-6b65e9bdc571 · outbound

This paper cites The open protocol standard for computerized building systems: Bacnet.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster The open protocol standard for computerized building systems: Bacnet

Reference 17

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Observation 851db4b3-bec3-485f-aabc-8a77009907b7 · outbound

This paper cites Haque, Yuxiong He, Sameh Elnikety, Thu D.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster Haque, Yuxiong He, Sameh Elnikety, Thu D

Reference 18

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Observation 80dd1f84-4ac8-458d-bac7-b6b9a562a16f · outbound

This paper cites SmoothOperator: Reducing Power Frag- mentation and Improving Power Utilization in Large- Scale Datacenters.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster SmoothOperator: Reducing Power Frag- mentation and Improving Power Utilization in Large- Scale Datacenters

Reference 19

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation a5607c7b-d31e-427f-99b3-0eb0a9cc872e · outbound

This paper cites WattWiser: Power & Resource-Efficient Schedul- ing for Multi-Model Multi-GPU Inference Servers.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster WattWiser: Power & Resource-Efficient Schedul- ing for Multi-Model Multi-GPU Inference Servers

Reference 20

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Observation d5f641c9-fbc4-4449-90f8-30ca6b0bdab6 · outbound

This paper cites SLO-aware GPU DVFS for Energy-efficient LLM Inference Serving.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster SLO-aware GPU DVFS for Energy-efficient LLM Inference Serving

Reference 21

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation abc2a1b5-6b84-4cb8-bf9d-e574bbf0119f · outbound

This paper cites AutoScale: Energy Efficiency Optimization for Stochastic Edge In- ference Using Reinforcement Learning.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster AutoScale: Energy Efficiency Optimization for Stochastic Edge In- ference Using Reinforcement Learning

Reference 22

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Source-reported events for the cited work

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Observation 8271f58c-3e54-4c70-b954-1d198b569685 · outbound

This paper cites Power capping of CPU-GPU heterogeneous systems through coordinat- ing DVFS and task mapping.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster Power capping of CPU-GPU heterogeneous systems through coordinat- ing DVFS and task mapping

Reference 23

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 2e5c2187-427e-4c18-b9bf-bc7ce9e92180 · outbound

This paper cites Tullsen, and Tajana Simunic Rosing.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster Tullsen, and Tajana Simunic Rosing

Reference 24

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation b9666016-0066-44ad-a2e6-eb3af8b3223c · outbound

This paper cites Misra, Seyyed Ahmad Javadi, Bianca Schroeder, Marcus Fontoura, and Ricardo Bianchini.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster Misra, Seyyed Ahmad Javadi, Bianca Schroeder, Marcus Fontoura, and Ricardo Bianchini

Reference 25

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation a1975e13-3468-49fa-9cae-50a7ec928fee · outbound

This paper cites Thunderbolt: Throughput-Optimized, Quality-of-Service-Aware Power Capping at Scale.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster Thunderbolt: Throughput-Optimized, Quality-of-Service-Aware Power Capping at Scale

Reference 26

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 5a0a0cc3-95e6-4338-b88f-e984db345475 · outbound

This paper cites Towards energy pro- portionality for large-scale latency-critical workloads.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster Towards energy pro- portionality for large-scale latency-critical workloads

Reference 27

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Source-reported events for the cited work

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Observation db14d7d8-2a7f-4a83-8701-cfee13f6b266 · outbound

This paper cites Virtual Machine series, April 2024.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster Virtual Machine series, April 2024

Reference 28

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation e7542757-e404-4e79-8d5c-7de647cf874a · outbound

This paper cites BatchSizer: Power-Performance Trade-off for DNN Inference.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster BatchSizer: Power-Performance Trade-off for DNN Inference

Reference 29

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation f48b1171-c191-4d65-8c75-3eb94d495892 · outbound

This paper cites Coordinated Batching and DVFS for DNN Inference on GPU Accelerators.IEEE Trans- actions on Parallel and Distributed Systems, 33(10), 2022.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster Coordinated Batching and DVFS for DNN Inference on GPU Accelerators.IEEE Trans- actions on Parallel and Distributed Systems, 33(10), 2022

Reference 30

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation b4c98298-c5f7-43e5-b200-9345d939a1f7 · outbound

This paper cites Twig: Multi-Agent Task Manage- ment for Colocated Latency-Critical Cloud Services.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster Twig: Multi-Agent Task Manage- ment for Colocated Latency-Critical Cloud Services

Reference 31

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation f2cd11b4-e76d-40cd-9f48-d4c2d47879fa · outbound

This paper cites OpenAI and NVIDIA An- nounce Strategic Partnership to Deploy 10 Gigawatts of NVIDIA Systems, 2025.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster OpenAI and NVIDIA An- nounce Strategic Partnership to Deploy 10 Gigawatts of NVIDIA Systems, 2025

Reference 32

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 2de02922-2cef-406a-8bc4-3653e82f5d72 · outbound

This paper cites OpenAI and Broadcom announce strategic col- laboration to deploy 10 gigawatts of OpenAI-designed AI accelerators, 2025.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster OpenAI and Broadcom announce strategic col- laboration to deploy 10 gigawatts of OpenAI-designed AI accelerators, 2025

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.577762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:03739e9f626d790e10717ffbb504701952db11fd0155601c81f1700c9e46a49f

Observation 66263c21-cd30-494f-a325-83bee8bd10a1 · outbound

This paper cites OpenAI, Oracle, and SoftBank expand Stargate with five new AI data center sites, 2025.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster OpenAI, Oracle, and SoftBank expand Stargate with five new AI data center sites, 2025

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.611543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:45cb6bec7f064ebc64a59102c8f1082a1e01721ed77ee29ebecfb98553bc395c

Observation 1c47f2c6-65ec-4783-aa8d-a3b0eef2bc7d · outbound

This paper cites Expanding datacenter capacity with dvfs boosting: A safe and scalable deployment ex- perience.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster Expanding datacenter capacity with dvfs boosting: A safe and scalable deployment ex- perience

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.564075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:dd8611922775745c575b841dd9c42a18cde2696d8647907e6c42916178f12a3c

Observation 0a806dd3-2a97-4f29-b51f-e27e2688c3ac · outbound

This paper cites Ranganathan, P.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster Ranganathan, P

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.550917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:62f1223bf3952c4055b11feef3975918bb32872a9bebc83b4fcdcf1a877c0f53

Observation 93fd5d00-5d15-4d4d-95b2-5493a11de0d0 · outbound

This paper cites Data Center Power Oversubscription with a Medium V olt- age Power Plane and Priority-Aware Capping.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster Data Center Power Oversubscription with a Medium V olt- age Power Plane and Priority-Aware Capping

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.625231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:c7b6eb198947559e736234e03a8d93b0d3623198de5855c8fe01f0b2df02ba50

Observation 1ba145c3-cc0f-429e-8665-e942798c788e · outbound

This paper cites From words to watts: Benchmarking the energy costs of large language model inference.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster From words to watts: Benchmarking the energy costs of large language model inference

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.685285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:afc9bfd22f0d1bebc3240d556773457edba753ee813033e2d25c961d63d421f3

Observation 8481cfbd-1fa3-4a3c-8ec3-c615c4c3fa60 · outbound

This paper cites EcoFaaS: Rethinking the Design of Serverless Environments for Energy Effi- ciency.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster EcoFaaS: Rethinking the Design of Serverless Environments for Energy Effi- ciency

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.681865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:528b176f8f4b1b111a274e603f007232b9f72eefda0ae89f7b4a6bbf98a39b56

Observation 5032e631-9972-4e92-96c3-00c8423b4553 · outbound

This paper cites SmartOClock: Workload- and Risk-Aware Overclocking in the Cloud.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster SmartOClock: Workload- and Risk-Aware Overclocking in the Cloud

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.616687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:87c709cc38fa6e3873167d85b0cdcb4220e0f33b3f831e44f1a84e861890ff73

Observation 2773df6f-a29e-472f-84e7-57db913be413 · outbound

This paper cites DynamoLLM: Designing LLM Inference Clusters for Performance and Energy Efficiency.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster DynamoLLM: Designing LLM Inference Clusters for Performance and Energy Efficiency

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.620516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:7ed82ceca650ef1e6059beb52a4877056aca2af927362869bf8e1c8c3cef6331

Observation f8a068a5-93e1-4dec-a6a0-289d090ce8c2 · outbound

This paper cites Rush, David Brooks, and Gu-Yeon Wei.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster Rush, David Brooks, and Gu-Yeon Wei

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.636166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:23e2785fd3f2e1a3a9de16b58d16f03e78d13460a95f99b603e36d54ab397dc5

Observation d1f328a5-5d23-4083-bff8-a974c344a6b8 · outbound

This paper cites The Impact of GPU DVFS on the En- ergy and Performance of Deep Learning: An Empirical Study.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster The Impact of GPU DVFS on the En- ergy and Performance of Deep Learning: An Empirical Study

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.608151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:a7b10a7fe7195f8f3f1eabfa535835d435f02a847cd58062e88693cd2094a375

Observation 05f915bf-af73-4bfa-bc05-af26ced3b124 · outbound

This paper cites Introduction to the modbus protocol.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster Introduction to the modbus protocol

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.584781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:45fd3bf71f4d9f035cfd2677bae27acf0eb3c9b6abc2b0f9a081c725a6c0283f

Observation 58a11846-e8f2-4fc5-ad2d-dc6ab45d4124 · outbound

This paper cites ALERT: Accurate learning for energy and timeliness.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster ALERT: Accurate learning for energy and timeliness

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.598998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:28c256f9d8c9ee94dcc9c67886a2515d5592ea1e059d60852698124ecf7b8efb

Observation 319ed90e-bf31-42b5-8da6-b6b7a0046b36 · outbound

This paper cites Dynamic GPU Energy Optimiza- tion for Machine Learning Training Workloads.IEEE Transactions on Parallel and Distributed Systems, 2022.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster Dynamic GPU Energy Optimiza- tion for Machine Learning Training Workloads.IEEE Transactions on Parallel and Distributed Systems, 2022

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.593470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:280ba5c89435cc6327bc26854af58357f8d0a0155c1c5bfad0b93444f8ea152d

Observation fe177fe5-3077-4ff5-b265-4fc6dddefeda · outbound

This paper cites Dynamo: Facebook’s Data Center- Wide Power Management System.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster Dynamo: Facebook’s Data Center- Wide Power Management System

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.596243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:4231051e5afc0fad453059f6525b0a617e1007baa915654f90d033427d01460a

Observation cf35ea9c-0b3b-4f9b-a448-c265c33ba85c · outbound

This paper cites Zeus: Understanding and optimizing GPU energy con- sumption of DNN training.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster Zeus: Understanding and optimizing GPU energy con- sumption of DNN training

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.589020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:f4fd31ab99022e4b74ca0df276da4a2231b47aa3f14aa3b3dc9949afcd8db02f

Observation 9859637f-7dbd-47fd-80ac-ce8730a418ef · outbound

This paper cites Know Your Enemy To Save Cloud Energy: Energy- Performance Characterization of Machine Learning Serving.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster Know Your Enemy To Save Cloud Energy: Energy- Performance Characterization of Machine Learning Serving

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.604210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:6bd3a427580496d2ba3803d30e8f72aaccdecfd373d0dec10ecc3ead3934b80b

Observation 552850f5-07ca-414b-bd96-c00b5ad8fa23 · outbound

This paper cites Misra, Rod As- sis, Kyle Woolcock, Nithish Mahalingam, Brijesh War- rier, David Gauthier, Lalu Kunnath, Steve Solomon, Os- valdo Morales, Marcus Fontoura, and Ricardo Bianchini.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster Misra, Rod As- sis, Kyle Woolcock, Nithish Mahalingam, Brijesh War- rier, David Gauthier, Lalu Kunnath, Steve Solomon, Os- valdo Morales, Marcus Fontoura, and Ricardo Bianchini

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.678376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:f7d07abb14406bed344e3c40b831f475afbd42d31571ec59074251bdbcd313e6

Observation 3c793dd0-275f-4d73-9467-88c580f645be · outbound

This paper cites Improving GPU Energy Efficiency through an Application-transparent Frequency Scaling Policy with Performance Assurance.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster Improving GPU Energy Efficiency through an Application-transparent Frequency Scaling Policy with Performance Assurance

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.586057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:b1bf26ac54332e8ebaad00a4818c75cc9f0fd761a12c04f7b365eeedac23b4e1

Observation 40604a14-0b78-4884-ae3d-1aab3d53e0b9 · outbound

This paper cites Indicator-Directed Dynamic Power Management for It- erative Workloads on GPU-Accelerated Systems.

Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster Indicator-Directed Dynamic Power Management for It- erative Workloads on GPU-Accelerated Systems

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.588616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:1f9c8344560385f5694567a6345a3375c048eeeca8b4befbe50ae5c382cfb70a

Pith citing papers

No inbound Pith citation observations are available.