Paper Citation Record · LEDGER

Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training

As of 11 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2501.11771.

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

pith.paper-citation-record.v1
2501.11771 v3

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T17:58:50.578176Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

36 of 36 outbound references displayed

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verified fuzzy28
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4b63f28e-f7db-4bb1-bb1d-b786580948f1 · outbound

This paper cites an unresolved cited work.

Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training Unresolved cited work

Reference 1

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Observation 36229877-9b51-496f-8607-25bb1c44cdb3 · outbound

This paper cites openssl: The open source toolkit for ssl/tls.

Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training openssl: The open source toolkit for ssl/tls

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:58:51.200960Z

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Observation ec662ec0-3241-4b14-97c4-676d5577cf23 · outbound

This paper cites Arm security technology building a secure system using trustzone technology,.

Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training Arm security technology building a secure system using trustzone technology,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:58:51.178233Z

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Observation c671f151-723e-49dd-914d-d373952ce3bd · outbound

This paper cites [Online].

Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training [Online]

Reference 4

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Observation 096bbb95-0005-487f-992c-0acdeb09d169 · outbound

This paper cites Amd secure encrypted virtualization (sev),.

Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training Amd secure encrypted virtualization (sev),

Reference 5

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Observation e2f5f6f5-f676-48b1-a865-e4c0e778bc1b · outbound

This paper cites AMD SEV-TIO: Trusted I/O for Secure Encrypted Virtualiza- tion,.

Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training AMD SEV-TIO: Trusted I/O for Secure Encrypted Virtualiza- tion,

Reference 6

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Observation 1d50c73b-0a7e-4c21-9fe6-62323622db5c · outbound

This paper cites Biased user history synthesis for personalized long- tail item recommendation,.

Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training Biased user history synthesis for personalized long- tail item recommendation,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:58:51.085240Z

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Observation 9b6826dd-6341-4e13-9271-59d17b63e3fd · outbound

This paper cites Logical/physical topology-aware col- lective communication in deep learning training,.

Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training Logical/physical topology-aware col- lective communication in deep learning training,

Reference 8

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

Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training Imagenet: A large-scale hierarchical image database,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:58:51.047902Z

Source-reported events for the cited work

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Observation 33b975a1-2906-4ef6-8455-87330486329b · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 10

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Source-reported events for the cited work

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Observation efaa7661-eddf-4af8-aee7-41648b6e0a8f · outbound

This paper cites Accelerate: Training and inference at scale made simple, efficient and adaptable.

Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training Accelerate: Training and inference at scale made simple, efficient and adaptable

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:58:51.031063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8f422dd7-5857-401a-9cf1-775b023b2004 · outbound

This paper cites Deep residual learning for image recognition,.

Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training Deep residual learning for image recognition,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:58:51.018350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1cffe10f-7d69-4e41-908a-b4efac65981c · outbound

This paper cites Gpipe: Efficient training of giant neural networks using pipeline parallelism,.

Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training Gpipe: Efficient training of giant neural networks using pipeline parallelism,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:58:51.004564Z

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Observation fc442cf9-79cf-4c07-ac64-879198b1d978 · outbound

This paper cites Intel trust domain extensions,.

Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training Intel trust domain extensions,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:58:50.987267Z

Source-reported events for the cited work

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Observation a1b6bf93-6481-43e9-9102-fcb785c4b8ce · outbound

This paper cites Intel TDX Connect TEE-IO Device Guide,.

Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training Intel TDX Connect TEE-IO Device Guide,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:58:50.970687Z

Source-reported events for the cited work

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Observation 44e0ca87-2bc2-483d-bfa5-b1daeeaff884 · outbound

This paper cites Hugging face,.

Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training Hugging face,

Reference 16

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Source-reported events for the cited work

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Observation 830d18c9-a941-4100-b3a7-c1b15f24fbca · outbound

This paper cites Intel software guard extensions: Epid provisioning and attestation services,.

Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training Intel software guard extensions: Epid provisioning and attestation services,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:58:50.941968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 671793b5-95dc-4b4d-8ec5-b20137417b09 · outbound

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

Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training PyTorch Distributed: Experiences on Accelerating Data Parallel Training

Reference 18

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Source-reported events for the cited work

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Observation 478fc7d2-eb57-4de7-86e1-78fc404d48db · outbound

This paper cites Azure confidential vms with nvidia h100 tensor core gpus,.

Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training Azure confidential vms with nvidia h100 tensor core gpus,

Reference 19

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Observation f6515a91-9724-467e-bd19-324f8579124d · outbound

This paper cites Securing ai inference in the cloud: Is cpu-gpu confidential computing ready?.

Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training Securing ai inference in the cloud: Is cpu-gpu confidential computing ready?

Reference 20

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Observation aa3c3e6b-b8ae-4a1d-9a70-afdd73b69b09 · outbound

This paper cites Supporting secure multi-gpu computing with dynamic and batched metadata management,.

Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training Supporting secure multi-gpu computing with dynamic and batched metadata management,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:58:50.889843Z

Source-reported events for the cited work

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Observation 33cef1dc-3ac0-4f89-9cb2-24d67e81fea5 · outbound

This paper cites CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models.

Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models

Reference 22

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Source-reported events for the cited work

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This paper cites Nvidia blackwell architecture technical brief,.

Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training Nvidia blackwell architecture technical brief,

Reference 23

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Observation 7d8d8969-10ee-4d52-a3e4-81ee0daccda2 · outbound

This paper cites Nvidia confidential computing,.

Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training Nvidia confidential computing,

Reference 24

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Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training Nvidia confidential computing protected pcie,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:58:50.845068Z

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Observation 2e54352e-6bb9-4634-9a4b-f6f133825eed · outbound

This paper cites Nvidia h100 tensor core gpu architecture overview,.

Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training Nvidia h100 tensor core gpu architecture overview,

Reference 26

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Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training Nvidia rtx pro blackwell architecture,

Reference 27

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Observation 527aa1eb-e8f3-4695-bd4a-9a2795d25de3 · outbound

This paper cites Language models are unsupervised multitask learners,.

Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training Language models are unsupervised multitask learners,

Reference 28

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Observation d4e52ba8-6504-4830-bf5d-ce5b9599149d · outbound

This paper cites Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters,.

Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters,

Reference 29

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This paper cites Performance analysis and optimization of nvidia h100 confidential computing for ai workloads,.

Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training Performance analysis and optimization of nvidia h100 confidential computing for ai workloads,

Reference 30

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Observation 817fff2e-ffe0-4021-9d50-3c7cdcfc4f00 · outbound

This paper cites Pipellm: Fast and confidential large language model services with speculative pipelined encryption,.

Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training Pipellm: Fast and confidential large language model services with speculative pipelined encryption,

Reference 31

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Observation d6d40d0d-adc5-4011-8745-606f73d9f83b · outbound

This paper cites GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding.

Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Reference 32

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Source-reported events for the cited work

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Observation cf779ad6-b6a7-4cb5-bde5-17d7eb3ac66f · outbound

This paper cites Fastrack: Fast IO for Secure ML using GPU TEEs.

Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training Fastrack: Fast IO for Secure ML using GPU TEEs

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-08-10T17:58:50.659311Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 02d4eb29-fc60-48df-ada0-245ea006972a · outbound

This paper cites Sampling-bias-corrected neural modeling for large corpus item recommendations,.

Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training Sampling-bias-corrected neural modeling for large corpus item recommendations,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:58:50.732582Z

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Observation d47e6977-df76-49ce-a46d-39e24005b275 · outbound

This paper cites PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel.

Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 35

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Observation 7180e3ba-c0a2-4e2f-9f3d-c9a428151ada · outbound

This paper cites Confidential Computing on NVIDIA Hopper GPUs: A Performance Benchmark Study.

Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training Confidential Computing on NVIDIA Hopper GPUs: A Performance Benchmark Study

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-10T17:58:50.578176Z

Source-reported events for the cited work

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Pith citing papers

No inbound Pith citation observations are available.