Pith. sign in

Paper Citation Record · LEDGER

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

As of 15 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 2026-08-15T06:32:42.880941+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

  • verified exact1
  • verified fuzzy48
  • unresolved3
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

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

Resolution
unresolved
raw_fallback, observed 2026-07-09T05:46:02.718250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:e7daea37594e0a91e7b506191d3bf6130b92cfe3e3a93c54dd0d6b88ff35f7aa

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

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.721287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:bb76f3ed2d5db747338716c23bf98a5419e9b650ad9e57a7cf71bb7dba9f2149

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

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.714709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:ef853d388bc9c2a1617648bd7aeb769aef8c053713975cfc7bb8809d0a51282c

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

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.728187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:6a2c286cdd9deecb426d34ebef3314c81610abe017fc56683d54b19fcc9e0780

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

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.734910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:651de81a815955ee3609801fcefa008bf241bf83e41069c96bf50f0ec4ef3494

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

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.708216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:5e550b76e5bb4a764ddd82d1bbc59d03753557860beabae08a28cf3556a17c94

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

Resolution
verified exact
arxiv_id, observed 2026-06-30T12:34:38.632486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:2fb16dc9f6d574b974601d6318fb5530be456557657125a750f55c62c2dfcad8

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

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.724894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:2e618c641e7e7c6c9c385582467868a928572b937c6ca8de060c4ddd1a69deba

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

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.704012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:c9fe661a6ce89974f4838c39427da4bab83ae9d1805afd9ef815ae021b18074b

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

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.711401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:7b8f55918abf7380868fd5aec16768a18b1e63709a2dd18ae16189888d41b86a

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

This paper cites an unresolved cited work.

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

Reference 11

Resolution
unresolved
raw_fallback, observed 2026-07-09T05:46:02.737846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:a635505c61b8351c9ec49d66ce3d3e7a1813994040a4a1a14113ece0673f3037

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

Resolution
unresolved
raw_fallback, observed 2026-07-09T05:46:02.693659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:6b08e08e8b595e9622576fb7d155ead6a228f16f87e2f7628551bae5b0cbb9ef

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

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.731647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:20f409540b7931f45fac74d94b7d49743bd1c1288486afe558d7d511aab16cc2

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

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.700286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:a0109529e924df2994f32e2bf9aee37a2a1ba6573caa4941ad075184205c037c

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

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.696434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:1ad58856c4d10dad61eeef33fa78ec371f38dccc1343bffcd9b36c85457533f7

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

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.580493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:e5a004ed1605c7cc58ff2e0e6c2aa7dc286ef800fb35b23d143e9f8f20fa8f22

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

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.670867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:402739cd655e9bcdf29035f04e077262e2b0ef1ca216934820dd12655f51c03f

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

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.548388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:96dcc4a87f74a923ac24f5d4ed11bf6dc045bf7f27e521873c3628b10346c18a

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

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.644858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:3043959a29f08fa52535de5dc302235b8ad1c80d49d6ec68d4f15af05407a5c3

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

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.631959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:5c3fc16dfcad33bdd67599e96d238a40913f1f190c4e5abb7b7cd49182b91bd2

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

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.661442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:69db0b32b35a5d561a08fe9c8d6505c38f06adf9a463a82219e9ed10e8d8f47a

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

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.591656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:9670014996fea0a4d5e7014d1c46004af171ff9c669c16e1d578d4826dde7a3b

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

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.572891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:ac4ff73da8cf217b20b91de342c9df3821a9aa8de53eef5e8ad9f25f9a3ec8d0

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

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.581321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:5eee0102ccddb8ea39a23a98b129dcfe2bcef7d763d5d8e53d732aa01de5fa88

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

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.583132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:516d4985ec2ec9e0ff29366e15a96323401ac61c1dd6c3c7e30f35362b743a3c

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

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.690389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:7f0d07f74e91639318920d0725d8547b858aea13897a0875666d16db60f9e55a

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

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.667205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:63fb899905b9d99811f8a41fcad198898ed4c0099fc658469f85887852760bae

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

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.657835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:7953fbac254192f709f6055c70c2dfa4edf33ec820bc31e32af35d8834d94878

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

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.570479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:6c15523dbb769013f63083512de0f523db833cb5eaf61975a82f317f10d2dc10

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

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.651439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:c8354f52405fd101f9bd6948543c59849a9c955e83d48fa01c1d19c2260661bb

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

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.575071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:5e6dce1200bde72a1dca2ec26f0cabb9c0cf345765495403e77574cf422ddcfb

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

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T05:46:02.545529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:d22181b45a691971f8a11d12f4c578c97e8ed32d2421c3f596c5a79e6b75a005

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:fcfc3f31536371e995e76b973b5999dc4fb46ed829c887a70973f74ab574602c

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:a8d497d4361f379a338451f60257fdfad6a275fa44007e8a7eea4da2423858d0

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:0f714d5c34a8d0f3b0aeb4c634889ace6446867a4163a3af9867726d7c0daf79

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:a3c4fb0de8a859a08a2a8149bc0a4b735889ba0a5ad394a5de446dcedd606dcc

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:a4028253a7dd3b469ab236b44f75f0db15b6db3fa9453554cdb0141564fbefeb

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:e01fbae122583ebd3e972a6cc6f9f6386ed94673911d3c6ad14cc65b5e6062a2

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:79ab048a35232552d0234708142f5fc772bd39f3dbbb107c55a65b1e43b62b63

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:0a8c461187a8bddcc1805471e46a1a5c47517360fbb3ba1fb59fb0cb1c8c5d35

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:2c179a407f1032982d5311d50606560711256e76e422c35432e7b6f9f6136095

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:ed8d95244c3fb485b1e238a4f9351a738738bbb15b7e9db808fd299df358f835

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:4b658e52f3a5d0f1edf2196e3a73d3ac6263a21da491c8616ecff773d22b1faa

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:bc05daa37b177a7cbf83c671903405a923c1565728822a09a71f81501caeb765

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:27a9cbc1cdfc6b958a5182b571d80f3e5b30a0ebf3a677492b647fcce2b660df

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:159c129ac330f294c6b04097644b702cce56bab67e9c45ec4356448c5a282760

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:11520f8c8c8e470fd3d506b6e463779f0c9ed1a1b97f9116122d4c2ad5fe8708

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:b28537ada2d2fe44950f3a478f16cf20557553dc66969b29e0c4e7ef532d7c3b

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:9327522eda458b398207f6a7b1771764c16af5a10c490d977af83826addfd3ee

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:1bdcd9d7d31f3c683c411b0cfa6d1e36ba77518d2b31c962de167b60be7762b8

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:57eb959e0f4ed9ac9c9296f2fc29e16db838262e1c174b2aef11a7e147a4bb48

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T12:30:57.065247Z digest=sha256:13fd3e8f6ccb1b12199b4c45d15b5e72a0670f9ce41049ee4c4f85d814b9f635

Pith citing papers

No inbound Pith citation observations are available.