Paper Citation Record · LEDGER

Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning

As of 18 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 4 inbound Pith citation observations for arXiv:2504.20854.

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

pith.paper-citation-record.v1
2504.20854 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:21:49.963514Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T12:09:21.778176Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T07:17:44.657994Z

Reference resolution

37 of 37 outbound references displayed

verified exact0
verified fuzzy34
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1e8e0d49-d99c-4871-8603-5dc15648cf08 · outbound

This paper cites an unresolved cited work.

Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-16T05:21:50.586052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 59902ddf-16d2-4e21-8fc7-b67786948c25 · outbound

This paper cites UEC White Paper.

Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning UEC White Paper

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:50.568040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation da8b1ca7-0555-4363-9c3f-def2237abf89 · outbound

This paper cites MSCCLang: Microsoft collective communication language.

Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning MSCCLang: Microsoft collective communication language

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:50.551465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4ae69c8e-6b3a-42d5-8b9b-8853c435e646 · outbound

This paper cites McMahon, Duncan Roweth, and Torsten Hoefler.

Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning McMahon, Duncan Roweth, and Torsten Hoefler

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:50.531384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 944f1ff1-f126-462d-aafd-4384a73e9076 · outbound

This paper cites Rocksdb: Evolution of development priorities in a key-value store serving large-scale applications.

Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning Rocksdb: Evolution of development priorities in a key-value store serving large-scale applications

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:50.511958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9c0acaa7-5874-40be-9f25-4fb3202b8786 · outbound

This paper cites DeepSeek-V3 Technical Report, 2025.

Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning DeepSeek-V3 Technical Report, 2025

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:50.496937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ec78ffa1-401d-45ca-b8ff-8f8c2f3d5482 · outbound

This paper cites an unresolved cited work.

Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-16T05:21:50.481820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 184da01c-1142-40bb-bc1e-1b1b4a9d71cd · outbound

This paper cites Com- piling machine learning programs via high-level trac- ing.

Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning Com- piling machine learning programs via high-level trac- ing

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:50.466182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7dde03c3-1d61-4cb4-b9a8-c2a5538c373e · outbound

This paper cites RDMA over Ethernet for Distributed Training at Meta Scale.

Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning RDMA over Ethernet for Distributed Training at Meta Scale

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:50.450747Z

Source-reported events for the cited work

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Observation d1e46e1e-d3a7-4e5b-88db-449cb6220f03 · outbound

This paper cites Google Falcon.

Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning Google Falcon

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:50.434737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 085f656d-049c-4676-b0a2-0ae6d8db5281 · outbound

This paper cites Infiniband Trade Associ- ation.

Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning Infiniband Trade Associ- ation

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:50.419549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ff59373f-72b7-47d9-802b-d0490043009a · outbound

This paper cites RDMA over Converged Ethernet.

Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning RDMA over Converged Ethernet

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:50.405376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8d9d7b32-ec5d-41a1-a65e-32cbed504c8e · outbound

This paper cites MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs.

Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:50.390858Z

Source-reported events for the cited work

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Observation 4a5d1336-e705-482f-b330-f872ce67688e · outbound

This paper cites Impact of RoCE Congestion Control Policies on Dis- tributed Training of DNNs.

Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning Impact of RoCE Congestion Control Policies on Dis- tributed Training of DNNs

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:50.373286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 054329b2-a864-4928-85fc-6d5df61d4d09 · outbound

This paper cites Accelerating Distributed MoE Training and Inference with Lina.

Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning Accelerating Distributed MoE Training and Inference with Lina

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:50.358013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 37d83e78-7f62-4bf5-9b71-e22dfce453ee · outbound

This paper cites Flor: An Open High Performance RDMA Framework Over Heterogeneous RNICs.

Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning Flor: An Open High Performance RDMA Framework Over Heterogeneous RNICs

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:50.343295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a5c473b5-3be4-43fa-8bb7-272931e944e8 · outbound

This paper cites Janus: A Unified Distributed Training Framework for Sparse Mixture-of-Experts Models.

Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning Janus: A Unified Distributed Training Framework for Sparse Mixture-of-Experts Models

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:50.327717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9c090500-e4d6-47cd-9817-65a551cb5c1e · outbound

This paper cites Chakra Working Group at MLCommons.

Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning Chakra Working Group at MLCommons

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:50.311423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 150ba5bc-aef7-44af-98e0-4021c137c491 · outbound

This paper cites Efficient large-scale language model training on gpu clusters using megatron-lm.

Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning Efficient large-scale language model training on gpu clusters using megatron-lm

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:50.295929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9f2bcfe8-b2f5-47d9-be62-90eb24f76159 · outbound

This paper cites NCCL Tests.

Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning NCCL Tests

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:50.280220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0de77ded-06eb-41d3-8823-b7c03547185b · outbound

This paper cites NVIDIA Spectrum-X Network Platform Ar- chitecture.

Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning NVIDIA Spectrum-X Network Platform Ar- chitecture

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:50.264913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 664a8d58-4d69-4ad7-97b2-b595c2963d4e · outbound

This paper cites NVIDIA Infiniband Adaptive Routing Technology—Accelerating HPC and AI Applications.

Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning NVIDIA Infiniband Adaptive Routing Technology—Accelerating HPC and AI Applications

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:50.250210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 21463079-f072-4fdb-99ec-c00469e15b2a · outbound

This paper cites libfabric.

Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning libfabric

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:50.235535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation bcbca0b8-f724-4195-8290-04662881c716 · outbound

This paper cites Perftest.

Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning Perftest

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:50.222926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 17cbc0c5-f039-4a9c-aa13-6a43ac58be47 · outbound

This paper cites Jupiter evolving: trans- forming google’s datacenter network via optical circuit switches and software-defined networking.

Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning Jupiter evolving: trans- forming google’s datacenter network via optical circuit switches and software-defined networking

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:50.208999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a0d5afa9-88b0-453b-8124-66619279b52c · outbound

This paper cites Alibaba hpn: A data center network for large language model training.

Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning Alibaba hpn: A data center network for large language model training

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:50.193257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 96a1519f-ac9b-49d2-b44c-014126654f90 · outbound

This paper cites CASSINI: Network-Aware job scheduling in machine learning clusters.

Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning CASSINI: Network-Aware job scheduling in machine learning clusters

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:50.176053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a9833016-2b2a-4113-b5a4-476066e2cc5a · outbound

This paper cites Deepspeed: System optimizations enable training deep learning models with over 100 billion pa- rameters.

Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning Deepspeed: System optimizations enable training deep learning models with over 100 billion pa- rameters

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:50.160627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f71a6308-f4a9-434c-a9b3-7bbb05e4ac2b · outbound

This paper cites libibverbs.

Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning libibverbs

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:50.145826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 16ecd48c-4da4-422a-9571-c2dfe13f6211 · outbound

This paper cites TACCL: Guiding collective algorithm synthesis using commu- nication sketches.

Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning TACCL: Guiding collective algorithm synthesis using commu- nication sketches

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:50.131433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2e2f04de-4022-43a7-932c-7fbe8aec14ef · outbound

This paper cites A Cloud-Optimized Transport Protocol for Elastic and Scalable HPC.

Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning A Cloud-Optimized Transport Protocol for Elastic and Scalable HPC

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:50.114169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6f79eea3-1e93-46a2-b57e-9c9659800906 · outbound

This paper cites Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces.

Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-16T05:21:49.944043Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4cd38868-01e8-4181-a16a-59a714721c6a · outbound

This paper cites TopoOpt: Co-optimizing network topology and parallelization strategy for dis- tributed training jobs.

Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning TopoOpt: Co-optimizing network topology and parallelization strategy for dis- tributed training jobs

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:50.097390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 26d11bdb-8d7a-423e-b685-caaf0ee99432 · outbound

This paper cites SimAI: Unifying Architecture Design and Performance Tunning for Large-Scale Large Lan- guage Model Training with Scalability and Precision.

Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning SimAI: Unifying Architecture Design and Performance Tunning for Large-Scale Large Lan- guage Model Training with Scalability and Precision

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:50.081300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5ebe9cb1-4d42-4bc6-936c-0a88aa1f0871 · outbound

This paper cites TACOS: Topology-aware collective algorithm synthesizer for dis- tributed machine learning.

Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning TACOS: Topology-aware collective algorithm synthesizer for dis- tributed machine learning

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:50.065253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6c9ea150-6c14-48ab-925a-3e45bdd27526 · outbound

This paper cites ASTRA-sim2.0: Modeling Hierarchical Networks and Disaggregated Systems for Large-model Training at Scale.

Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning ASTRA-sim2.0: Modeling Hierarchical Networks and Disaggregated Systems for Large-model Training at Scale

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:50.050183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1cc34892-1675-47ff-8373-7bc4163affd6 · outbound

This paper cites Towards a Standardized Representation for Deep Learning Collective Algorithms.

Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning Towards a Standardized Representation for Deep Learning Collective Algorithms

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:50.034376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

Observation b196572c-6a6e-44d1-98a5-b9ce303a1e5e · inbound

Flint: Compiler Enabled Cluster-Free Design Space Exploration for Distributed ML cites this paper.

Flint: Compiler Enabled Cluster-Free Design Space Exploration for Distributed ML Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-10T05:25:55.186774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 30a1f307-028e-4e74-bf14-cd5d4e40d4da · inbound

MLCommons Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces cites this paper.

MLCommons Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning

Reference 104

Resolution: verified exact; arxiv_id, observed 2026-05-13T01:47:05.072390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6b77dd6f-5120-49e0-b004-805f78f23dd1 · inbound

MLCommons Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces cites this paper.

MLCommons Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning

Reference 104

Resolution: verified exact; arxiv_id, observed 2026-05-20T22:19:07.521618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e3c7f028-4e56-403f-b74c-d14d98ed7837 · inbound

ASTRA-sim 3.0: Next-Level Distributed Machine Learning Simulations via High-Fidelity GPU and Infrastructure Modeling cites this paper.

ASTRA-sim 3.0: Next-Level Distributed Machine Learning Simulations via High-Fidelity GPU and Infrastructure Modeling Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning

Reference 56

Resolution: verified exact; arxiv_id, observed 2026-07-03T07:17:44.659418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.