Paper Citation Record · LEDGER

Profiling and optimization of multi-card GPU machine learning jobs

As of 8 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2505.22905.

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

pith.paper-citation-record.v1
2505.22905 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:00:40.815419Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

37 of 37 outbound references displayed

verified exact2
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 79abec30-b5d6-4146-895c-5549be18ffd2 · outbound

This paper cites https://mitsloan.mit.edu/ideas- made-to-matter/ai-has-high-data-center-energy-costs-there-are-solutions.

Profiling and optimization of multi-card GPU machine learning jobs https://mitsloan.mit.edu/ideas- made-to-matter/ai-has-high-data-center-energy-costs-there-are-solutions

Reference 1

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Observation a63c442b-aa95-497c-8780-40f469cbd791 · outbound

This paper cites Beyond T raditional Learning: The LLM Revolution in BPM Education at Univer- sity.

Profiling and optimization of multi-card GPU machine learning jobs Beyond T raditional Learning: The LLM Revolution in BPM Education at Univer- sity

Reference 2

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Observation 7a823e98-3226-4418-8b00-d7b2a3c41534 · outbound

This paper cites The Language Model Revolution: LLM and SLM Analysis.

Profiling and optimization of multi-card GPU machine learning jobs The Language Model Revolution: LLM and SLM Analysis

Reference 3

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Observation dd29ea58-6b2f-48d9-819e-1bbe8fb0de7e · outbound

This paper cites A New Golden Age in Computer Architecture: Empowering the Machine-Learning Revo- lution.

Profiling and optimization of multi-card GPU machine learning jobs A New Golden Age in Computer Architecture: Empowering the Machine-Learning Revo- lution

Reference 4

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Observation a8ec2b11-debc-489b-9bfb-bfb88e0263e9 · outbound

This paper cites Towards Universal Performance Modeling for Machine Learning Training on Multi-GPU Platforms.

Profiling and optimization of multi-card GPU machine learning jobs Towards Universal Performance Modeling for Machine Learning Training on Multi-GPU Platforms

Reference 5

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Observation 43ad643f-6410-41ec-8c5d-ae5d0007e147 · outbound

This paper cites TwinPilots: A New Computing Paradigm for GPU-CPU Parallel LLM Inference.

Profiling and optimization of multi-card GPU machine learning jobs TwinPilots: A New Computing Paradigm for GPU-CPU Parallel LLM Inference

Reference 6

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Source-reported events for the cited work

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Observation 1f101d7c-d933-4d80-b814-a508ab91a60f · outbound

This paper cites Understanding the eﬃciency of GPU algorithms for matrix-matrix multiplication.

Profiling and optimization of multi-card GPU machine learning jobs Understanding the eﬃciency of GPU algorithms for matrix-matrix multiplication

Reference 7

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Source-reported events for the cited work

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Observation 14f184db-b71e-4717-a172-cf8c6ba0087d · outbound

This paper cites Optimization and architecture eﬀects on GPU computing workload performance.

Profiling and optimization of multi-card GPU machine learning jobs Optimization and architecture eﬀects on GPU computing workload performance

Reference 8

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Observation 383c89ae-1ee0-4e22-98a3-361d7a876527 · outbound

This paper cites Coordinating the use of GPU and CPU for im- proving performance of compute intensive applications.

Profiling and optimization of multi-card GPU machine learning jobs Coordinating the use of GPU and CPU for im- proving performance of compute intensive applications

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:00:45.787213Z

Source-reported events for the cited work

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Observation 312bd2de-3c62-42e4-bd67-185f4411e3ef · outbound

This paper cites Comparing Energy Eﬃciency of CPU, GPU and FPGA Im- plementations for Vision Kernels.

Profiling and optimization of multi-card GPU machine learning jobs Comparing Energy Eﬃciency of CPU, GPU and FPGA Im- plementations for Vision Kernels

Reference 10

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Source-reported events for the cited work

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Observation c8d9a13b-4a56-4750-80d9-0ccee4dc866c · outbound

This paper cites A comparative study of GPU programming models and architectures using neural networks.

Profiling and optimization of multi-card GPU machine learning jobs A comparative study of GPU programming models and architectures using neural networks

Reference 11

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Source-reported events for the cited work

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Observation 56b53104-31e2-4fd0-8a08-225d23c00b57 · outbound

This paper cites Optimizing Performance of Recurrent Neural Networks on GPUs.

Profiling and optimization of multi-card GPU machine learning jobs Optimizing Performance of Recurrent Neural Networks on GPUs

Reference 12

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Observation 9017212e-523b-4e7c-9372-402532a4af64 · outbound

This paper cites PyT orch distributed: experiences on accelerating data parallel training.

Profiling and optimization of multi-card GPU machine learning jobs PyT orch distributed: experiences on accelerating data parallel training

Reference 13

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Observation c8f3c744-d27d-4a5c-b8e5-db344a7f89a2 · outbound

This paper cites https://top500.org/system/180290/.

Profiling and optimization of multi-card GPU machine learning jobs https://top500.org/system/180290/

Reference 14

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Source-reported events for the cited work

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Observation de3f46b1-9c8a-4758-8d29-d451467a3e6c · outbound

This paper cites https://docs.nvidia.com/datacenter/nvtags/1.1/nvtags- user-guide/index.html.

Profiling and optimization of multi-card GPU machine learning jobs https://docs.nvidia.com/datacenter/nvtags/1.1/nvtags- user-guide/index.html

Reference 15

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Observation e1d9bd2a-f9d6-44a6-aa9a-ee18911a44eb · outbound

This paper cites https://pytorch.org/tutorials/intermediate/ddp_tutorial.

Profiling and optimization of multi-card GPU machine learning jobs https://pytorch.org/tutorials/intermediate/ddp_tutorial

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:00:44.573618Z

Source-reported events for the cited work

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Observation 90d9a0c9-d759-45bc-88ad-714653297708 · outbound

This paper cites https://pytorch.org/torchtune/stable/deep_dives/configs.

Profiling and optimization of multi-card GPU machine learning jobs https://pytorch.org/torchtune/stable/deep_dives/configs

Reference 17

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Source-reported events for the cited work

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Observation 8b54a6d0-a690-4a0a-97c0-1951ff949acc · outbound

This paper cites https://developer.nvidia.com/nsight-systems.

Profiling and optimization of multi-card GPU machine learning jobs https://developer.nvidia.com/nsight-systems

Reference 18

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Source-reported events for the cited work

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Observation ba51cef9-4d14-4416-849c-3eb5413a027c · outbound

This paper cites High-Performance Data Loader for Large-Scale Data Processing.

Profiling and optimization of multi-card GPU machine learning jobs High-Performance Data Loader for Large-Scale Data Processing

Reference 19

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Observation fe0e72c9-2db0-4e0e-8021-0dbfa4b211ce · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Profiling and optimization of multi-card GPU machine learning jobs Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 20

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Observation 08db4c51-ebef-4c73-b095-5090f32e3a75 · outbound

This paper cites https://www.kaggle.com/datasets/hojjatk/mnist-dataset.

Profiling and optimization of multi-card GPU machine learning jobs https://www.kaggle.com/datasets/hojjatk/mnist-dataset

Reference 21

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Observation f8ed8111-713b-4f6c-bdab-8728d4fb9a03 · outbound

This paper cites https://keras.io/api/applications/mobilenet/.

Profiling and optimization of multi-card GPU machine learning jobs https://keras.io/api/applications/mobilenet/

Reference 22

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Observation c6475942-5a1a-4aa5-87e2-8bc7442a2e3e · outbound

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Profiling and optimization of multi-card GPU machine learning jobs U-Net: Convolutional Networks for Biomedical Image Segmentation

Reference 23

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Observation 653bbf00-9f6b-4d2e-abde-450295d12f56 · outbound

This paper cites Attention-based Image Upsampling.

Profiling and optimization of multi-card GPU machine learning jobs Attention-based Image Upsampling

Reference 24

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Observation 7ecc26db-c763-4545-8b15-5e2a707fcfd8 · outbound

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Profiling and optimization of multi-card GPU machine learning jobs Unresolved cited work

Reference 25

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Observation 31e1d91c-31d2-4671-a5c1-df979b246feb · outbound

This paper cites An Emotion T ext Classiﬁcation Model Based on Llama3-8b Using Lora T echnique.

Profiling and optimization of multi-card GPU machine learning jobs An Emotion T ext Classiﬁcation Model Based on Llama3-8b Using Lora T echnique

Reference 26

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Observation 217690b4-34c2-477f-8770-bf0ace27beca · outbound

This paper cites Large Language Models: A Survey.

Profiling and optimization of multi-card GPU machine learning jobs Large Language Models: A Survey

Reference 27

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This paper cites GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints.

Profiling and optimization of multi-card GPU machine learning jobs GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints

Reference 28

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Profiling and optimization of multi-card GPU machine learning jobs The Llama 3 Herd of Models

Reference 29

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This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Profiling and optimization of multi-card GPU machine learning jobs LoRA: Low-Rank Adaptation of Large Language Models

Reference 30

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This paper cites Direct Preference Optimization: Your Language Model is Secretly a Reward Model.

Profiling and optimization of multi-card GPU machine learning jobs Direct Preference Optimization: Your Language Model is Secretly a Reward Model

Reference 31

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Observation 226f01c1-b68d-4ee7-9b5e-f6cdf74bda42 · outbound

This paper cites QLoRA: Efficient Finetuning of Quantized LLMs.

Profiling and optimization of multi-card GPU machine learning jobs QLoRA: Efficient Finetuning of Quantized LLMs

Reference 32

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Profiling and optimization of multi-card GPU machine learning jobs Unresolved cited work

Reference 33

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Observation 1c5bac2f-87cf-4c6b-8c96-6b44f98116bf · outbound

This paper cites Synthetic Data Generation for Grammatical Error Correction with T agged Corruption Models.

Profiling and optimization of multi-card GPU machine learning jobs Synthetic Data Generation for Grammatical Error Correction with T agged Corruption Models

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:00:42.419253Z

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This paper cites SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Sum- marization.

Profiling and optimization of multi-card GPU machine learning jobs SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Sum- marization

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:00:42.243206Z

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Observation d92b94a0-902d-491a-9748-97d44710e280 · outbound

This paper cites CUF: Continuous Upsampling Filters.

Profiling and optimization of multi-card GPU machine learning jobs CUF: Continuous Upsampling Filters

Reference 2022

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Observation 0071c3d8-ba13-4378-ae35-16f1f2949067 · outbound

This paper cites Optimizing Large Language Models through Quantization: A Comparative Analysis of PTQ and QAT Techniques.

Profiling and optimization of multi-card GPU machine learning jobs Optimizing Large Language Models through Quantization: A Comparative Analysis of PTQ and QAT Techniques

Reference 2024

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Source-reported events for the cited work

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Pith citing papers

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