Paper Citation Record · LEDGER

Energy Consumption in Parallel Neural Network Training

As of 10 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2508.07706.

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

pith.paper-citation-record.v1
2508.07706 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T21:59:30.126019Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

38 of 38 outbound references displayed

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verified fuzzy6
unresolved23
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External citation measurements

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Outbound references

Observation f371a397-1a4d-46c4-b066-1311d37c6a3a · outbound

This paper cites Demystifying parallel and distributed deep learning: An in-depth concurrency analysis,.

Energy Consumption in Parallel Neural Network Training Demystifying parallel and distributed deep learning: An in-depth concurrency analysis,

Reference 1

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Observation 67e313f9-16e9-4e14-9924-21993522d4ef · outbound

This paper cites Distributed training of deep neural networks: Theoretical and practical limits of parallel scalability,.

Energy Consumption in Parallel Neural Network Training Distributed training of deep neural networks: Theoretical and practical limits of parallel scalability,

Reference 2

Resolution: verified exact; doi, observed 2026-08-05T21:59:31.099753Z

Source-reported events for the cited work

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Observation 00359b5e-e90d-474c-aa93-d23da34106c5 · outbound

This paper cites PyTorch Distributed: Experiences on Accelerating Data Parallel Training.

Energy Consumption in Parallel Neural Network Training PyTorch Distributed: Experiences on Accelerating Data Parallel Training

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-05T21:59:27.320114Z

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Observation a709d46e-aa97-4069-bf43-1fcc1e4fac44 · outbound

This paper cites Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour.

Energy Consumption in Parallel Neural Network Training Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T21:59:27.378608Z

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Observation a1f2a9c6-63bc-434c-b70e-d4e9b35d6ff6 · outbound

This paper cites Large Batch Training of Convolutional Networks.

Energy Consumption in Parallel Neural Network Training Large Batch Training of Convolutional Networks

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-05T21:59:27.531983Z

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Observation 0bc787ed-712b-42e5-b8b2-6d0418f4fef8 · outbound

This paper cites Yet Another Accelerated SGD: ResNet-50 Training on ImageNet in 74.7 seconds.

Energy Consumption in Parallel Neural Network Training Yet Another Accelerated SGD: ResNet-50 Training on ImageNet in 74.7 seconds

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T21:59:27.562665Z

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Observation 427ac881-5cd6-4533-8a14-cc364338b7ac · outbound

This paper cites On the sdes and scaling rules for adaptive gradient algorithms,.

Energy Consumption in Parallel Neural Network Training On the sdes and scaling rules for adaptive gradient algorithms,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:59:32.807898Z

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Observation 50ccec83-acd6-45b1-8434-323c2e0ab803 · outbound

This paper cites Reporting electricity consumption is essential for sustainable ai,.

Energy Consumption in Parallel Neural Network Training Reporting electricity consumption is essential for sustainable ai,

Reference 8

Resolution: verified exact; doi, observed 2026-08-05T21:59:30.863080Z

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Observation b3776e7e-c861-4aa9-83b0-4d07f2d0c814 · outbound

This paper cites Energy costs of communicating with ai,.

Energy Consumption in Parallel Neural Network Training Energy costs of communicating with ai,

Reference 9

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Observation 6c64d018-14c2-4c42-ae82-6cce68dbb2d2 · outbound

This paper cites Deep residual learning for image recognition,.

Energy Consumption in Parallel Neural Network Training Deep residual learning for image recognition,

Reference 10

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Observation e897bcd8-9554-47ef-804e-fe3301208637 · outbound

This paper cites Imagenet: A large-scale hierarchical image database,.

Energy Consumption in Parallel Neural Network Training Imagenet: A large-scale hierarchical image database,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T21:59:27.974322Z

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Observation 19120c0c-cb6f-476f-8d5b-2f11fdb2c7e4 · outbound

This paper cites FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators.

Energy Consumption in Parallel Neural Network Training FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-05T21:59:28.055078Z

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Observation 356f55b8-36f4-40ce-9052-5a88fd280233 · outbound

This paper cites Fourcastnet,.

Energy Consumption in Parallel Neural Network Training Fourcastnet,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:59:32.671958Z

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Observation f6f28c14-69fe-4059-a0ad-8369f7204003 · outbound

This paper cites The era5 global reanalysis,.

Energy Consumption in Parallel Neural Network Training The era5 global reanalysis,

Reference 14

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Observation 2d4e2918-c97f-4778-aea9-4a5aaba9d453 · outbound

This paper cites Green ai,.

Energy Consumption in Parallel Neural Network Training Green ai,

Reference 15

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Observation 5ccaa54f-aac0-4b69-8f69-ed743031315c · outbound

This paper cites Quantifying the carbon emissions of machine learning,.

Energy Consumption in Parallel Neural Network Training Quantifying the carbon emissions of machine learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:59:32.562391Z

Source-reported events for the cited work

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Observation ad7c6112-3499-418f-986b-5da523c5078c · outbound

This paper cites Towards energy-efficient Deep Learning: An overview of energy-efficient approaches along the Deep Learning Lifecycle.

Energy Consumption in Parallel Neural Network Training Towards energy-efficient Deep Learning: An overview of energy-efficient approaches along the Deep Learning Lifecycle

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-05T21:59:28.450074Z

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Observation 7f1a4b88-27f9-435d-8852-26034dd31492 · outbound

This paper cites Energy Efficiency of Training Neural Network Architectures: An Empirical Study.

Energy Consumption in Parallel Neural Network Training Energy Efficiency of Training Neural Network Architectures: An Empirical Study

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-05T21:59:28.579827Z

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Observation 53845b63-b8e3-442c-9ce6-0d1353f403c1 · outbound

This paper cites Measuring the Energy Consumption and Efficiency of Deep Neural Networks: An Empirical Analysis and Design Recommendations.

Energy Consumption in Parallel Neural Network Training Measuring the Energy Consumption and Efficiency of Deep Neural Networks: An Empirical Analysis and Design Recommendations

Reference 19

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Observation 833f9304-ece3-48ec-a975-f6e8bc70db61 · outbound

This paper cites Zeus: Understanding and optimizing GPU energy consumption of DNN training,.

Energy Consumption in Parallel Neural Network Training Zeus: Understanding and optimizing GPU energy consumption of DNN training,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:59:32.431352Z

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Observation b7bc85a2-51b8-42bb-bf1a-4338f9005273 · outbound

This paper cites Precise energy consumption measurements of heterogeneous artificial intelligence workloads,.

Energy Consumption in Parallel Neural Network Training Precise energy consumption measurements of heterogeneous artificial intelligence workloads,

Reference 21

Resolution: verified exact; doi, observed 2026-08-05T21:59:30.662721Z

Source-reported events for the cited work

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Observation d756c0f1-cadc-4862-bd94-34842ca00735 · outbound

This paper cites Uncovering energy- efficient practices in deep learning training: Preliminary steps towards green ai,.

Energy Consumption in Parallel Neural Network Training Uncovering energy- efficient practices in deep learning training: Preliminary steps towards green ai,

Reference 22

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Observation 1ac57e61-ab90-431e-9b8d-b8fae579316b · outbound

This paper cites Spend more to save more (sm2): An energy-aware implementation of successive halving for sustainable hyperpa- rameter optimization,.

Energy Consumption in Parallel Neural Network Training Spend more to save more (sm2): An energy-aware implementation of successive halving for sustainable hyperpa- rameter optimization,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:59:32.349660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6c619feb-4d48-4ac7-9249-e56bbd84f995 · outbound

This paper cites The power of training: How different neural network setups influence the energy demand,.

Energy Consumption in Parallel Neural Network Training The power of training: How different neural network setups influence the energy demand,

Reference 24

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Observation de1dba3a-c4c4-4b62-bff6-06d3518170be · outbound

This paper cites Benchmarking resource usage for efficient distributed deep learning,.

Energy Consumption in Parallel Neural Network Training Benchmarking resource usage for efficient distributed deep learning,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-05T21:59:29.228207Z

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Observation 02a721d2-33ef-4627-8850-023c56a40281 · outbound

This paper cites Performance and energy aware training of a deep neural network in a multi-gpu environment with power capping,.

Energy Consumption in Parallel Neural Network Training Performance and energy aware training of a deep neural network in a multi-gpu environment with power capping,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-05T21:59:29.266258Z

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Observation 39a78f93-62ff-421f-8049-18203beaf1dd · outbound

This paper cites Communication-Efficient Large-Scale Distributed Deep Learning: A Comprehensive Survey.

Energy Consumption in Parallel Neural Network Training Communication-Efficient Large-Scale Distributed Deep Learning: A Comprehensive Survey

Reference 27

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Observation b0c3ba07-4152-4328-b3b2-0768c001e06c · outbound

This paper cites On efficient training of large-scale deep learning models,.

Energy Consumption in Parallel Neural Network Training On efficient training of large-scale deep learning models,

Reference 28

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Observation b79b2359-a693-4685-b767-7a07d31143b6 · outbound

This paper cites PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization,.

Energy Consumption in Parallel Neural Network Training PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:59:32.220641Z

Source-reported events for the cited work

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Observation c4b60a6d-fa26-4245-9e97-74b0596a7389 · outbound

This paper cites Learned Gradient Compression for Distributed Deep Learning,.

Energy Consumption in Parallel Neural Network Training Learned Gradient Compression for Distributed Deep Learning,

Reference 30

Resolution: metadata mismatch; raw_fallback, observed 2026-08-05T21:59:31.664240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f7a37f95-4a06-4189-86ce-60f1e2912d17 · outbound

This paper cites HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent.

Energy Consumption in Parallel Neural Network Training HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-08-05T21:59:30.417365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8b256303-7dcf-4db7-b4b1-7c3de58b4a0e · outbound

This paper cites Don't Use Large Mini-Batches, Use Local SGD.

Energy Consumption in Parallel Neural Network Training Don't Use Large Mini-Batches, Use Local SGD

Reference 32

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Observation 744b8164-e385-4fe1-a956-c347ef016450 · outbound

This paper cites Accelerating neural network training with distributed asynchronous and selective optimization (daso),.

Energy Consumption in Parallel Neural Network Training Accelerating neural network training with distributed asynchronous and selective optimization (daso),

Reference 33

Resolution: malformed identifier; no resolver link, observed 2026-08-05T21:59:29.765634Z

Source-reported events for the cited work

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Observation d19973b9-498c-4292-a4f2-a7b39629def9 · outbound

This paper cites Efficient data-parallel distributed dnn train- ing for big dataset under heterogeneous gpu cluster,.

Energy Consumption in Parallel Neural Network Training Efficient data-parallel distributed dnn train- ing for big dataset under heterogeneous gpu cluster,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-05T21:59:29.815036Z

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Unavailable: canonical work link unavailable.

Observation a3a67f4c-03ef-4ce0-8f8d-d7c967d6f517 · outbound

This paper cites An empirical study of training self-supervised vision transformers,.

Energy Consumption in Parallel Neural Network Training An empirical study of training self-supervised vision transformers,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-05T21:59:29.862419Z

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Observation 1e2f485a-6e15-4fd4-a5bb-4c04b6ebaf77 · outbound

This paper cites Fourcastnet: Accelerating global high- resolution weather forecasting using adaptive fourier neural operators,.

Energy Consumption in Parallel Neural Network Training Fourcastnet: Accelerating global high- resolution weather forecasting using adaptive fourier neural operators,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-05T21:59:29.956300Z

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Unavailable: canonical work link unavailable.

Observation 69be2eda-f064-4629-a89d-5da9fbd6ac17 · outbound

This paper cites Perun: Benchmarking energy consumption of high-performance computing applications,.

Energy Consumption in Parallel Neural Network Training Perun: Benchmarking energy consumption of high-performance computing applications,

Reference 37

Resolution: verified exact; doi, observed 2026-08-05T21:59:30.257164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fb70ed2a-5bac-4d0e-aade-367028f1c443 · outbound

This paper cites Generalized Slow Roll for Tensors.

Energy Consumption in Parallel Neural Network Training Generalized Slow Roll for Tensors

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-05T21:59:30.126019Z

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Pith citing papers

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