Paper Citation Record · LEDGER

Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2211.13878.

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

pith.paper-citation-record.v1
2211.13878 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:20:50.830441Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T09:59:45.779352Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 212b88bf-4fd2-44cb-aa25-822f9488130a · inbound

Rethinking Dynamic Networks and Heterogeneous Computing with Automatic Parallelization cites this paper.

Rethinking Dynamic Networks and Heterogeneous Computing with Automatic Parallelization Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T11:20:50.830441Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9187a99-8bee-4347-a13b-3a75ff5a9d5f · inbound

OctoPipe: Reducing Pipeline Bubbles for Heterogeneous Models via Co-Optimizing Partitioning, Placement, and Scheduling cites this paper.

OctoPipe: Reducing Pipeline Bubbles for Heterogeneous Models via Co-Optimizing Partitioning, Placement, and Scheduling Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-04T14:43:01.294927Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 375f4a72-ba38-414c-bf02-ea84228555c2 · inbound

Autopoiesis: A Self-Evolving System Paradigm for LLM Serving Under Runtime Dynamics cites this paper.

Autopoiesis: A Self-Evolving System Paradigm for LLM Serving Under Runtime Dynamics Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-05-11T06:41:17.615643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 451ccf74-a7d9-4dc7-8c1a-2fb58b37f9cd · inbound

FEPLB: Exploiting Copy Engines for Nearly Free MoE Load Balancing in Distributed Training cites this paper.

FEPLB: Exploiting Copy Engines for Nearly Free MoE Load Balancing in Distributed Training Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-11T13:41:05.466156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 880bfd4e-5e4c-492d-9bc2-d338a3384700 · inbound

HexiSeq: Accommodating Long Context Training of LLMs over Heterogeneous Hardware cites this paper.

HexiSeq: Accommodating Long Context Training of LLMs over Heterogeneous Hardware Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:00:55.678755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 28976e67-9f84-4676-83dd-e844bb66414c · inbound

HexAGenT: Efficient Agentic LLM Serving via Workflow- and Heterogeneity-Aware Scheduling cites this paper.

HexAGenT: Efficient Agentic LLM Serving via Workflow- and Heterogeneity-Aware Scheduling Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-19T21:02:47.406324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation dcba0c51-3c7b-4f04-b769-4e61ac8c383e · inbound

Frontier: Towards Comprehensive and Accurate LLM Inference Simulation cites this paper.

Frontier: Towards Comprehensive and Accurate LLM Inference Simulation Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-21T03:49:31.073602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e2b278f7-2bcf-4a6f-a5c0-f22e8ad4e498 · inbound

LiveR: Fine-Grained Elasticity via Live Reconfiguration for Model Training cites this paper.

LiveR: Fine-Grained Elasticity via Live Reconfiguration for Model Training Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-22T04:31:03.955731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9a730153-3192-4064-8f26-bf1ccfa68fc1 · inbound

Piper: A Programmable Distributed Training System cites this paper.

Piper: A Programmable Distributed Training System Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-07-03T07:57:44.637394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5a3df194-1fbb-4f69-8da9-8454a94bfb9b · inbound

FlowTrain: Flow-Based Decoupled Training for Industrial-Grade Vision-Language Models cites this paper.

FlowTrain: Flow-Based Decoupled Training for Industrial-Grade Vision-Language Models Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism

Reference 13

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T09:59:45.780630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.