Paper Citation Record · LEDGER

Faster Multi-GPU Training with PPLL: A Pipeline Parallelism Framework Leveraging Local Learning

As of 18 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 2 inbound Pith citation observations for arXiv:2411.12780.

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

pith.paper-citation-record.v1
2411.12780 v1

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T17:46:22.492350Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T00:46:33.892664Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T06:23:05.567137Z

Reference resolution

17 of 17 outbound references displayed

verified exact2
verified fuzzy9
unresolved6
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation be191c76-300a-4aff-a669-b52c7fac450c · outbound

This paper cites Decoupled greedy learning of cnns.

Faster Multi-GPU Training with PPLL: A Pipeline Parallelism Framework Leveraging Local Learning Decoupled greedy learning of cnns

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:46:23.001070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8b950314-cc5d-4be6-9b05-fcafb287dc2a · outbound

This paper cites A fast learning algorithm for deep belief nets.

Faster Multi-GPU Training with PPLL: A Pipeline Parallelism Framework Leveraging Local Learning A fast learning algorithm for deep belief nets

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T17:46:22.413440Z

Source-reported events for the cited work

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Observation f9f5d467-c807-442a-beca-8e65e63d804e · outbound

This paper cites Learning mul- tiple layers of features from tiny images.

Faster Multi-GPU Training with PPLL: A Pipeline Parallelism Framework Leveraging Local Learning Learning mul- tiple layers of features from tiny images

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T17:46:22.423009Z

Source-reported events for the cited work

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Observation c94664f4-cdd5-472b-8fb6-4195be9e97f1 · outbound

This paper cites Reading digits in natural images with unsupervised feature learning.

Faster Multi-GPU Training with PPLL: A Pipeline Parallelism Framework Leveraging Local Learning Reading digits in natural images with unsupervised feature learning

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:46:22.938272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8e3e52fe-c5af-4c9f-af10-3abbecfa2846 · outbound

This paper cites An analy- sis of single-layer networks in unsupervised feature learn- ing.

Faster Multi-GPU Training with PPLL: A Pipeline Parallelism Framework Leveraging Local Learning An analy- sis of single-layer networks in unsupervised feature learn- ing

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:46:22.898085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation fc9e05cf-7bc7-4527-ae10-cd72c8795bf3 · outbound

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

Faster Multi-GPU Training with PPLL: A Pipeline Parallelism Framework Leveraging Local Learning Gpipe: Efficient training of giant neural networks using pipeline parallelism

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:46:22.879258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0ed766aa-42dd-4634-b708-847f1183383a · outbound

This paper cites Daniel Hillis and Guy L.

Faster Multi-GPU Training with PPLL: A Pipeline Parallelism Framework Leveraging Local Learning Daniel Hillis and Guy L

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:46:22.855214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b6c1aecf-4205-4b3b-a36a-2adc34d12755 · outbound

This paper cites Imagenet classification with deep convolutional neural networks.

Faster Multi-GPU Training with PPLL: A Pipeline Parallelism Framework Leveraging Local Learning Imagenet classification with deep convolutional neural networks

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-12T17:46:22.446428Z

Source-reported events for the cited work

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Observation 34cf963b-9214-4bba-9b21-d1b4f45fd7cb · outbound

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

Faster Multi-GPU Training with PPLL: A Pipeline Parallelism Framework Leveraging Local Learning PyTorch Distributed: Experiences on Accelerating Data Parallel Training

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-12T17:46:22.452219Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d3b95ca-7d3d-4ae4-bcd5-79e9fa371113 · outbound

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

Faster Multi-GPU Training with PPLL: A Pipeline Parallelism Framework Leveraging Local Learning PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T17:46:22.457941Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3bda2cad-809a-4ede-a8f3-b3799923503c · outbound

This paper cites Local plasticity rules can learn deep representations using self-supervised contrastive predic- tions.

Faster Multi-GPU Training with PPLL: A Pipeline Parallelism Framework Leveraging Local Learning Local plasticity rules can learn deep representations using self-supervised contrastive predic- tions

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:46:22.808711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 175cb347-ef0e-4d15-af2b-75d0ce86842b · outbound

This paper cites Loco: Local contrastive representation learning.

Faster Multi-GPU Training with PPLL: A Pipeline Parallelism Framework Leveraging Local Learning Loco: Local contrastive representation learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:46:22.786571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b3cadd0c-1a09-4917-bef6-ffe909d5740d · outbound

This paper cites Local to global learning: Gradually adding classes for training deep neural networks.

Faster Multi-GPU Training with PPLL: A Pipeline Parallelism Framework Leveraging Local Learning Local to global learning: Gradually adding classes for training deep neural networks

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:46:22.760710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7a3fc108-8b2c-45b4-80e7-569bd04bbc3a · outbound

This paper cites Fedbr: Im- proving federated learning on heterogeneous data via lo- cal learning bias reduction.

Faster Multi-GPU Training with PPLL: A Pipeline Parallelism Framework Leveraging Local Learning Fedbr: Im- proving federated learning on heterogeneous data via lo- cal learning bias reduction

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:46:22.733854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d57830d4-4029-430d-a672-54f0f11470b2 · outbound

This paper cites Local Learning with Neuron Groups.

Faster Multi-GPU Training with PPLL: A Pipeline Parallelism Framework Leveraging Local Learning Local Learning with Neuron Groups

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-12T17:46:22.609378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d9400c90-e3fc-4737-bc91-7c476b5364c0 · outbound

This paper cites Momentum Auxiliary Network for Supervised Local Learning.

Faster Multi-GPU Training with PPLL: A Pipeline Parallelism Framework Leveraging Local Learning Momentum Auxiliary Network for Supervised Local Learning

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-12T17:46:22.569857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 98d25200-3573-470e-b797-135d5ac747e5 · outbound

This paper cites Deep residual learning for image recognition.

Faster Multi-GPU Training with PPLL: A Pipeline Parallelism Framework Leveraging Local Learning Deep residual learning for image recognition

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-12T17:46:22.492350Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 9215886a-27a1-49a3-94ce-5aeecc4a9a33 · inbound

Replacement Learning: Training Neural Networks with Fewer Parameters cites this paper.

Replacement Learning: Training Neural Networks with Fewer Parameters Faster Multi-GPU Training with PPLL: A Pipeline Parallelism Framework Leveraging Local Learning

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-20T06:23:05.568643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 740843f9-c526-482d-b412-87ce05d8d282 · inbound

ZeroLock: Concurrent Memory-Efficient LLM Training via Modular Update Decoupling cites this paper.

ZeroLock: Concurrent Memory-Efficient LLM Training via Modular Update Decoupling Faster Multi-GPU Training with PPLL: A Pipeline Parallelism Framework Leveraging Local Learning

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-12T00:46:33.892664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.