Paper Citation Record · LEDGER

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights

As of 22 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 1 inbound Pith citation observation for arXiv:2412.12426.

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

pith.paper-citation-record.v1
2412.12426 v2

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T14:11:43.902914Z

measured 58 of 58 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T19:04:25.951890Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T23:30:51.664114Z

Reference resolution

57 of 57 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c893a1fa-6f19-4890-9e91-aa653e7e679a · outbound

This paper cites Introducing the AI Research Su- perCluster — Meta’s cutting-edge AI supercomputer for AI research,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights Introducing the AI Research Su- perCluster — Meta’s cutting-edge AI supercomputer for AI research,

Reference 1

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Observation 6fa1daad-6df9-4626-8b31-7c6549fd2705 · outbound

This paper cites Microsoft announces new supercomputer, lays out vision for future AI work,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights Microsoft announces new supercomputer, lays out vision for future AI work,

Reference 2

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Observation 988b9baf-bd1f-4320-a253-b6f4db0d48ac · outbound

This paper cites Frontier,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights Frontier,

Reference 3

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Observation f1ed3ccc-3760-4442-a190-19383220c164 · outbound

This paper cites POLCA: Power Oversubscription in LLM Cloud Providers.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights POLCA: Power Oversubscription in LLM Cloud Providers

Reference 4

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Observation 306707e1-de69-45b6-97e5-ed2f397b3935 · outbound

This paper cites Towards improved power management in cloud gpus,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights Towards improved power management in cloud gpus,

Reference 5

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Observation 6a4ac66b-e19d-497d-bb58-e53dcbd8fe5a · outbound

This paper cites Accurate and Convenient Energy Measurements for GPUs: A Detailed Study of NVIDIA GPU’s Built-In Power Sensor,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights Accurate and Convenient Energy Measurements for GPUs: A Detailed Study of NVIDIA GPU’s Built-In Power Sensor,

Reference 6

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Observation c099751a-41d9-478c-8057-4059781752a9 · outbound

This paper cites MI300X powers LLaMA405 at Meta,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights MI300X powers LLaMA405 at Meta,

Reference 7

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Observation d4650b33-5c63-4302-9650-498e2961a08e · outbound

This paper cites 11.1 AMD Instinct™ MI300 Series Modular Chiplet Package – HPC and AI Accel- erator for Exa-Class Systems,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights 11.1 AMD Instinct™ MI300 Series Modular Chiplet Package – HPC and AI Accel- erator for Exa-Class Systems,

Reference 8

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Observation 82695bf3-55fe-4cf6-925e-4550bddfcf11 · outbound

This paper cites AMD Instinct™MI300X Accelerator: Packaging and Architecture Co-Optimization,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights AMD Instinct™MI300X Accelerator: Packaging and Architecture Co-Optimization,

Reference 9

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Observation 01ab5b14-0bfb-413f-ae10-bd687db0c635 · outbound

This paper cites The AMD CDNA ™ 3 architecture,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights The AMD CDNA ™ 3 architecture,

Reference 10

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Observation 57d7209f-66d1-4447-a284-8b42c5094e02 · outbound

This paper cites Constraint-Driven Innovation,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights Constraint-Driven Innovation,

Reference 11

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Observation aae390b0-eb88-4ee9-8bdc-5622e821115c · outbound

This paper cites How much electricity does an American home use?.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights How much electricity does an American home use?

Reference 12

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Observation b44fd566-cc15-4ea5-ac4d-9f8aa41742b5 · outbound

This paper cites Tale of Two Cs: Computation vs. Communication Scaling for Future Transformers on Future Hardware,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights Tale of Two Cs: Computation vs. Communication Scaling for Future Transformers on Future Hardware,

Reference 13

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Observation 8c8b7346-dde2-46b7-b0e4-a088ef1adfd7 · outbound

This paper cites AMD SMI documentation,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights AMD SMI documentation,

Reference 14

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Observation 668d5c5d-95d5-4c6e-977c-c2fb1bbfef1c · outbound

This paper cites AMD ROCm ™ Software,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights AMD ROCm ™ Software,

Reference 15

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Observation 2e772d6e-5332-4d80-8921-3b4d6b5c119b · outbound

This paper cites ROCm ™/rocBLAS: Next generation BLAS implementation for ROCm™ platform,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights ROCm ™/rocBLAS: Next generation BLAS implementation for ROCm™ platform,

Reference 16

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Source-reported events for the cited work

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Observation 4c1eb5a3-a530-401d-8a07-7c3166b56aad · outbound

This paper cites ROCm ™ Communication Collectives Library,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights ROCm ™ Communication Collectives Library,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:11:44.825146Z

Source-reported events for the cited work

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Observation 1900e8a6-ed73-4b2b-a2cf-6f41f62e81bc · outbound

This paper cites NanoFlow: Towards Optimal Large Language Model Serving Throughput.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights NanoFlow: Towards Optimal Large Language Model Serving Throughput

Reference 18

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Observation 01cc29de-79a8-45ed-8200-e4aa4d555f85 · outbound

This paper cites System Management Interface SMIn,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights System Management Interface SMIn,

Reference 19

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Observation bb7cb8ba-7648-4ce8-9f9a-945c8f7f48f6 · outbound

This paper cites Variorum,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights Variorum,

Reference 20

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Observation 882fd00c-a87a-4084-8793-b15b887b0e50 · outbound

This paper cites Standardizing Power Monitoring and Control at Exascale,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights Standardizing Power Monitoring and Control at Exascale,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:11:44.777772Z

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Observation 7199c6ac-3d54-40a0-a211-0f46cd342672 · outbound

This paper cites PowerSensor 2: A Fast Power Mea- surement Tool,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights PowerSensor 2: A Fast Power Mea- surement Tool,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:11:44.761608Z

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Observation 5d7654ea-8e96-4175-a56d-c1d438fe1a2f · outbound

This paper cites Measuring GPU Power with the K20 Built-in Sensor,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights Measuring GPU Power with the K20 Built-in Sensor,

Reference 23

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Observation ba45680b-3085-4141-b9ee-6e391d8a6205 · outbound

This paper cites Towards Accurate and Reliable Energy Measurement of NLP Models.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights Towards Accurate and Reliable Energy Measurement of NLP Models

Reference 24

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Observation 0853d164-c241-47a7-8e74-15bf8691092d · outbound

This paper cites A Comparative Study of Techniques for Energy Predictive Modeling Using Performance Monitoring Counters on Modern Multicore CPUs,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights A Comparative Study of Techniques for Energy Predictive Modeling Using Performance Monitoring Counters on Modern Multicore CPUs,

Reference 25

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Observation 52473b1c-6a65-46a0-8fe0-0be6f08f510e · outbound

This paper cites An experimental comparison of software-based power me- ters: focus on CPU and GPU,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights An experimental comparison of software-based power me- ters: focus on CPU and GPU,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:11:44.709897Z

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Observation 86523f57-de75-471a-8669-86dd2f2278d8 · outbound

This paper cites AccelWattch: A Power Modeling Framework for Modern GPUs,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights AccelWattch: A Power Modeling Framework for Modern GPUs,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:11:44.692198Z

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Observation fb49a11b-3566-4cf3-ab6c-89d3ca6be5d8 · outbound

This paper cites Understanding the Future of Energy Efficiency in Multi-Module GPUs,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights Understanding the Future of Energy Efficiency in Multi-Module GPUs,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:11:44.675650Z

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Observation 6d0ca992-2e3c-4277-9dfd-7927f6d059c6 · outbound

This paper cites Measuring and modeling on-chip interconnect power on real hardware,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights Measuring and modeling on-chip interconnect power on real hardware,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:11:44.658602Z

Source-reported events for the cited work

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Observation e0344a36-bf95-4fa4-b7bd-c98241f656ed · outbound

This paper cites Power and Performance Characterization and Modeling of GPU-Accelerated Systems,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights Power and Performance Characterization and Modeling of GPU-Accelerated Systems,

Reference 30

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Observation 19953486-3f32-4f00-95f2-ccfd15c2c97b · outbound

This paper cites Online Power Estimation of Graphics Processing Units,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights Online Power Estimation of Graphics Processing Units,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:11:44.625580Z

Source-reported events for the cited work

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Observation 35f420ff-56fb-4b47-ab41-e35a739fe9e5 · outbound

This paper cites GPGPU performance and power estimation using machine learning,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights GPGPU performance and power estimation using machine learning,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:11:44.607981Z

Source-reported events for the cited work

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Observation b8383415-5bb3-41a1-bea6-285078669cb2 · outbound

This paper cites High-Resolution Power Profiling of GPU Functions Using Low-Resolution Measurement,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights High-Resolution Power Profiling of GPU Functions Using Low-Resolution Measurement,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:11:44.590985Z

Source-reported events for the cited work

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Observation f3b1bc08-cee8-4fde-8ebe-1c74ac960510 · outbound

This paper cites Optimizing performance-per-watt on GPUs in high performance com- puting: Temperature, frequency and voltage effects,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights Optimizing performance-per-watt on GPUs in high performance com- puting: Temperature, frequency and voltage effects,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:11:44.573489Z

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Observation fb746387-4914-4f5c-a16f-51fafd8c55a3 · outbound

This paper cites Benchmarking the Performance and Energy Efficiency of AI Acceler- ators for AI Training,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights Benchmarking the Performance and Energy Efficiency of AI Acceler- ators for AI Training,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:11:44.554513Z

Source-reported events for the cited work

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Observation 3d1ebe3a-1ae6-4401-aaee-0862f9e92e3b · outbound

This paper cites Efficiency Near the Edge: Increasing the Energy Efficiency of FFTs on GPUs for Real-Time Edge Computing,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights Efficiency Near the Edge: Increasing the Energy Efficiency of FFTs on GPUs for Real-Time Edge Computing,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:11:44.537515Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 92efd8df-2eb5-4570-b175-759176c51888 · outbound

This paper cites GPU-NEST: Characterizing Energy Efficiency of Multi-GPU Inference Servers,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights GPU-NEST: Characterizing Energy Efficiency of Multi-GPU Inference Servers,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:11:44.521167Z

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Observation b93b48f7-ee05-4a8a-b346-7b6766a9f17e · outbound

This paper cites Carbon Emissions and Large Neural Network Training.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights Carbon Emissions and Large Neural Network Training

Reference 38

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Source-reported events for the cited work

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Observation 854d28d0-836b-41b5-98a6-f4dcc38da829 · outbound

This paper cites Cutting the cost of pulsar astronomy: Saving time and energy when searching for binary pulsars using NVIDIA GPUs.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights Cutting the cost of pulsar astronomy: Saving time and energy when searching for binary pulsars using NVIDIA GPUs

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-08-11T14:11:44.185674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation dd068719-25d2-42eb-b386-d58016779d31 · outbound

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

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights Know Your Enemy To Save Cloud Energy: Energy-Performance Characterization of Machine Learning Serving,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:11:44.502624Z

Source-reported events for the cited work

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Observation d61e9cd7-d3a0-41e2-a9d2-0b781edf8d6e · outbound

This paper cites On the Rise of AMD Matrix Cores: Performance, Power Efficiency, and Programmability,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights On the Rise of AMD Matrix Cores: Performance, Power Efficiency, and Programmability,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:11:44.486257Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 84d5d1ef-bc0d-4ea0-8e81-2f261ede08ae · outbound

This paper cites A measurement study of GPU DVFS on energy conservation,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights A measurement study of GPU DVFS on energy conservation,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:11:44.467041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 80c2cd53-25cd-4ed2-8acf-7cff2307ac33 · outbound

This paper cites Comparing GPU Power and Frequency Capping: A Case Study with the MuMMI Workflow,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights Comparing GPU Power and Frequency Capping: A Case Study with the MuMMI Workflow,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:11:44.446013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6837584a-555c-4bec-b98b-f8f0603cd5b8 · outbound

This paper cites The Impact of GPU DVFS on the Energy and Performance of Deep Learning: an Empirical Study,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights The Impact of GPU DVFS on the Energy and Performance of Deep Learning: an Empirical Study,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:11:44.420728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0fa6752c-535d-4965-95f1-c0868003599c · outbound

This paper cites Performance/Energy Aware Optimiza- tion of Parallel Applications on GPUs Under Power Capping,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights Performance/Energy Aware Optimiza- tion of Parallel Applications on GPUs Under Power Capping,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:11:44.402046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 30b2bdf6-b6eb-4727-b7e7-19c281e66877 · outbound

This paper cites Input-Dependent Power Usage in GPUs.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights Input-Dependent Power Usage in GPUs

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:43.847470Z

Source-reported events for the cited work

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Observation eedf7422-5a65-4f2e-aaa2-7519daee0d0e · outbound

This paper cites Dynamic GPGPU Power Management Using Adaptive Model Predictive Control,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights Dynamic GPGPU Power Management Using Adaptive Model Predictive Control,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:11:44.384531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e45cee16-704f-424a-93bb-17686230ba37 · outbound

This paper cites Predict; Do not React for Enabling Efficient Fine Grain DVFS in GPUs.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights Predict; Do not React for Enabling Efficient Fine Grain DVFS in GPUs

Reference 48

Resolution: verified exact; local_arxiv, observed 2026-08-11T14:11:44.134697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d964a6e9-b823-4e91-83cd-3d9e08f4c758 · outbound

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

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights Improving GPU Energy Efficiency through an Application-transparent Frequency Scaling Policy with Performance Assurance,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:11:44.365812Z

Source-reported events for the cited work

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Observation efc28964-8f32-4580-be37-d50552505404 · outbound

This paper cites DRLCAP: Runtime GPU Frequency Capping With Deep Reinforce- ment Learning,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights DRLCAP: Runtime GPU Frequency Capping With Deep Reinforce- ment Learning,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:11:44.345046Z

Source-reported events for the cited work

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Observation f4c41695-9415-42bd-945d-b559525ec573 · outbound

This paper cites Going green: optimizing GPUs for energy efficiency through model-steered auto-tuning.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights Going green: optimizing GPUs for energy efficiency through model-steered auto-tuning

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-08-11T14:11:44.107802Z

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Observation 0b8885ba-3619-403f-8f31-124fd6aa79a8 · outbound

This paper cites Energy-Aware Tile Size Selection for Affine Programs on GPUs,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights Energy-Aware Tile Size Selection for Affine Programs on GPUs,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:11:44.326765Z

Source-reported events for the cited work

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Observation cf6754ed-9403-4367-8854-e737ad0fb0cf · outbound

This paper cites Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:11:44.310113Z

Source-reported events for the cited work

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Observation 5c4088fb-637f-4ef7-bd9d-94c838be4474 · outbound

This paper cites Reducing Energy Bloat in Large Model Training,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights Reducing Energy Bloat in Large Model Training,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:11:44.292730Z

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Observation 8d0ded29-decc-4f67-aceb-0eee893e8081 · outbound

This paper cites EnvPipe: Performance- preserving DNN training framework for saving energy,.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights EnvPipe: Performance- preserving DNN training framework for saving energy,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:11:44.276327Z

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Observation 9afc3bd6-322b-438d-90e3-62acef38b9b8 · outbound

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

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights DynamoLLM: Designing LLM Inference Clusters for Performance and Energy Efficiency,

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:43.897992Z

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Observation 060f7aaf-4582-44d0-ac39-f5712625c59a · outbound

This paper cites Splitwise: Efficient generative LLM inference using phase splitting.

FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights Splitwise: Efficient generative LLM inference using phase splitting

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:43.902914Z

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Pith citing papers

Observation 83082f4e-ced4-4947-9d30-b3759a3897d5 · inbound

The Energy Cost of Execution-Idle in GPU Clusters cites this paper.

The Energy Cost of Execution-Idle in GPU Clusters FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-10T23:30:51.668047Z

Source-reported events for the cited work

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