Paper Citation Record · LEDGER

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput

As of 10 August 2026, this Paper Citation Record lists 76 of 76 outbound references and 4 inbound Pith citation observations for arXiv:2502.06982.

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

pith.paper-citation-record.v1
2502.06982 v2

Coverage vector

measured 76 of 76 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T14:14:35.448676Z

measured 80 of 80 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:57:52.568195Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T21:36:15.542964Z

Reference resolution

76 of 76 outbound references displayed

verified exact2
verified fuzzy8
unresolved61
parse uncertain0
malformed identifier1
metadata mismatch4

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cea38c78-cb23-4e32-891e-8cfc71194c40 · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:14:37.099828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0b4dea27-9028-45d8-a809-974e48dd30b7 · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:14:37.087597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ba93c145-4b2e-4506-89aa-11ff69ed5b23 · outbound

This paper cites TensorFlow: A system for large-scale machine learning.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput TensorFlow: A system for large-scale machine learning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-08T14:14:35.173870Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25394408-4efc-4f53-b8ec-bde0f8a2f269 · outbound

This paper cites Banning, Sumeer Bhola, Rick Buskens, Ming Chen, Xi Chen, Yoo Chung, Qin Jia, Nick Sakharov, George T.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Banning, Sumeer Bhola, Rick Buskens, Ming Chen, Xi Chen, Yoo Chung, Qin Jia, Nick Sakharov, George T

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:14:37.074250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 62504617-2bff-4208-b436-f941d37f44c7 · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:14:37.061577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 31b47966-264d-4026-820a-a17fd17da18e · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-08T14:14:35.185891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0bee877f-3334-4be6-b069-8149d26f08bf · outbound

This paper cites Pathways: Asynchronous Distributed Dataflow for ML.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Pathways: Asynchronous Distributed Dataflow for ML

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-08T14:14:35.190079Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6aa35d2a-d3ab-4c23-80fa-86e629d65c28 · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:14:37.049550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c6639a02-af13-4600-b624-732fa7140ab8 · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:14:37.033407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6a921add-11f4-4482-9a0c-3307790786b9 · outbound

This paper cites Cooper, and Linda Torczon.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Cooper, and Linda Torczon

Reference 10

Resolution: metadata mismatch; raw_fallback, observed 2026-08-08T14:14:36.380989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b2da1d18-b349-4355-abe8-ac2c17255023 · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:14:37.018889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3d8a7939-5e56-4b69-8c42-c3783a43f834 · outbound

This paper cites Language Models are Few-Shot Learners.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Language Models are Few-Shot Learners

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-08T14:14:35.209953Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06eea592-b20f-4e4b-8d92-9c1fd16bf40b · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-08T14:14:35.214069Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f709f81-142b-4e48-b57a-23358e43ca57 · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:14:36.998340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3249a713-8368-4699-b59e-6da122993ffa · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-08T14:14:35.217913Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1cdcf7a7-8a22-45ad-bcc6-363b710d7867 · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:14:36.973987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3da094ce-d419-4776-90da-4cf7a41b2857 · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 17

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:14:36.986304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 271ae118-3e51-49a6-8c22-5fca3a4c9e90 · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:14:36.963328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5d71858a-c111-4394-91c4-3007c6bd2bb5 · outbound

This paper cites Emer and Douglas W.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Emer and Douglas W

Reference 19

Resolution: metadata mismatch; raw_fallback, observed 2026-08-08T14:14:36.283984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3ada0bbe-4023-4306-949b-5ad8a1d8acc4 · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 20

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:14:36.939914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 44db8799-8f3c-4dc5-badf-05346876a859 · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:14:36.951919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 87fb889a-4061-43d3-bf8e-740bf237af1d · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:14:36.916849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation aeaf1fd6-7599-4aaf-8326-02135d738274 · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 23

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:14:36.928462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3fb2793b-0597-426b-835a-8a363aa6a5ab · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:14:36.890153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 18c5a57b-994b-45ff-ae44-aeb52ba82a7c · outbound

This paper cites Hennessy and David A.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Hennessy and David A

Reference 25

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fa0088d6-7fc3-43aa-b781-393a2d03c52e · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:14:36.865391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fab075ab-53f4-40d7-8332-10a85963d716 · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 27

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:14:36.878769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a383b0ba-cb36-47ff-a4f3-5a3c501ef85f · outbound

This paper cites TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddings.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddings

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-08T14:14:35.272822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a04529fe-3d6e-4562-a63d-1f61baacdcc2 · outbound

This paper cites Jouppi, Doe Hyun Yoon, Matthew Ashcraft, Mark Gottscho, Thomas B.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Jouppi, Doe Hyun Yoon, Matthew Ashcraft, Mark Gottscho, Thomas B

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-08T14:14:35.269272Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9abe851-84b4-4daf-96cf-3ffaf96d86d2 · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 30

Resolution: metadata mismatch; raw_fallback, observed 2026-08-08T14:14:36.116560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7e35396e-d937-4097-995a-bb982854c260 · outbound

This paper cites Jouppi, Cliff Young, Nishant Patil, David A.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Jouppi, Cliff Young, Nishant Patil, David A

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:14:36.852533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 04723395-cbe5-4fd1-9ddc-be9bcb5f78dc · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 32

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:14:36.825662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 40174fd1-2797-43c3-ae8e-0389fe5574ee · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 33

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:14:36.811728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8aa90ba5-3253-4e87-9805-7f31fa76a548 · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 34

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:14:36.838478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4ac63b1a-3cd3-4886-8bbc-eb203aa1bbfd · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 35

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:14:36.786613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 68f558cb-ed27-41ae-bfcc-4886d4f215bd · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 36

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:14:36.762357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation edde4ab9-8bd1-46dd-8c57-a6843fd05f34 · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 37

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:14:36.799045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cd04e0e4-912f-47fb-a144-b30e5033e5a5 · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 38

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:14:36.728319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ab82423b-1998-4c8e-bc28-340e0a3c3a3e · outbound

This paper cites Mustafa Rafique, Franck Cappello, and Bogdan Nicolae.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Mustafa Rafique, Franck Cappello, and Bogdan Nicolae

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-08T14:14:35.326881Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b89ee389-a29c-4bf0-adc7-c68a0b78d1f3 · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 40

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:14:36.716124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1fd3196a-60c5-4b1d-b348-6451670d219c · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-08T14:14:35.315520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8522ad1-6365-45dc-9a64-2d62889e1620 · outbound

This paper cites Deep Learning Recommendation Model for Personalization and Recommendation Systems.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Deep Learning Recommendation Model for Personalization and Recommendation Systems

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-08T14:14:35.337895Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46a99af2-e73d-43ec-bd45-7fc432253e3d · outbound

This paper cites Wozniak, George Bosilca, Matthieu Dorier, and Franck Cappello.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Wozniak, George Bosilca, Matthieu Dorier, and Franck Cappello

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-08T14:14:35.341659Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7582782-e7b2-40e7-aa22-f70f980fc257 · outbound

This paper cites Li, Ryan McElroy, Mike Paleczny, Daniel Peek, Paul Saab, David Stafford, Tony Tung, and Venkateshwaran Venkataramani.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Li, Ryan McElroy, Mike Paleczny, Daniel Peek, Paul Saab, David Stafford, Tony Tung, and Venkateshwaran Venkataramani

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:14:36.703233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e0bd02fb-8097-4562-94ec-647cb41c74ff · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 45

Resolution: verified exact; raw_fallback, observed 2026-08-08T14:14:35.862508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0ecf53de-5d5d-4554-aa1e-9b436101f1bf · outbound

This paper cites tf.data: A Machine Learning Data Processing Framework.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput tf.data: A Machine Learning Data Processing Framework

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-08T14:14:35.334066Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4364c9d7-070c-4f5b-9c59-84e7e8ba1517 · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-08T14:14:35.356616Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f925e958-84f0-4b51-ac10-d7dce73ce6dd · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 48

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:14:36.678965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 47937785-8b0a-4ee3-ab3f-aeec2bed55c5 · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-08T14:14:35.364656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0baefb9-e41d-4293-a820-61eca9060050 · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 50

Resolution: metadata mismatch; raw_fallback, observed 2026-08-08T14:14:35.763370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c3a6bd37-7003-4a44-a5c7-ce46522edf43 · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 51

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:14:36.691185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fe464644-1d9c-4018-b17d-0f4c6115eb9f · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 52

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:14:36.634749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 890106eb-518b-4e56-ba70-5d9027342048 · outbound

This paper cites an unresolved cited work.

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Reference 53

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Observation d26cc555-a91d-4763-94ad-c2d91c7904f3 · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 54

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Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput In Proceedings of Ma- chine Learning and Systems , D

Reference 55

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Observation 5906e66f-f389-487c-b4ce-db3b8e78b1b2 · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 56

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Observation ec8ef137-7b5d-4351-ace9-ab1db94c451f · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:14:36.646435Z

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This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Gemini: A Family of Highly Capable Multimodal Models

Reference 58

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Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 59

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Observation 904ed2ba-818e-456a-b955-e57284ea0b3a · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 60

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Observation ac4921a7-110b-4bfb-85dc-a81c9c424164 · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 61

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:14:36.609749Z

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Observation 1c7deb3f-fc0a-47f7-aeee-3d452d9d5be2 · outbound

This paper cites Korupolu, David Oppenheimer, Eric Tune, and John Wilkes.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Korupolu, David Oppenheimer, Eric Tune, and John Wilkes

Reference 62

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1bdd4758-21ba-4026-91b0-1158d0faa395 · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:14:36.527478Z

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Observation 6118bd45-25b0-4fc1-914f-1ddd3305fad0 · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:14:36.585251Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fdf12497-143c-417d-9602-cacddbb99a31 · outbound

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Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput An Evaluation of Edge TPU Accelerators for Convolutional Neural Networks

Reference 65

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Observation 39d43346-6e2b-489e-b7cb-18c1aa711eda · outbound

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Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Yoo, Morris A

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:14:36.515371Z

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Observation 10ca42e5-1ffb-4ccd-ba5b-00e93d4f239b · outbound

This paper cites Understanding Data Storage and Ingestion for Large-Scale Deep Recommendation Model Training.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Understanding Data Storage and Ingestion for Large-Scale Deep Recommendation Model Training

Reference 67

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Observation 3c795c23-a7ed-4002-a3c5-1ffc392fa3d6 · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-08T14:14:35.417476Z

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Unavailable: canonical work link unavailable.

Observation db9cc7a8-a2bb-4a3e-ab44-a1e8936db368 · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 69

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:14:36.502598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 758981c4-9a02-457b-af2b-5a42ce20c145 · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-08T14:14:35.429521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc3ae17a-213b-4917-98b6-7e871893365c · outbound

This paper cites an unresolved cited work.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Unresolved cited work

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-08T14:14:35.444334Z

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Unavailable: canonical work link unavailable.

Observation 2dfe1f75-e9ed-41f0-a3d4-35e47168f5fb · outbound

This paper cites In Proceedings of the 44th annual IEEE/ACM International Symposium on Microarchitecture.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput In Proceedings of the 44th annual IEEE/ACM International Symposium on Microarchitecture

Reference 2011

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:14:36.741531Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a8f6a0e4-5c66-44ee-ab88-74857485dbf5 · outbound

This paper cites TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-08T14:14:35.169430Z

Source-reported events for the cited work

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Observation 82e0ff50-14c6-4299-ae00-bc1275b92bbc · outbound

This paper cites In-Datacenter Performance Analysis of a Tensor Processing Unit.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput In-Datacenter Performance Analysis of a Tensor Processing Unit

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-08T14:14:35.280728Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39ad9462-27af-42f6-a0ae-b7ac694bcc37 · outbound

This paper cites Learned Hardware/Software Co-Design of Neural Accelerators.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput Learned Hardware/Software Co-Design of Neural Accelerators

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-08T14:14:35.395499Z

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Observation 2ba9a89e-3730-4418-9db9-519699aa3567 · outbound

This paper cites In International Conference on High Performance Computing.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput In International Conference on High Performance Computing

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:14:36.774606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

Observation 41d4bd1b-5ccd-4729-80ed-24c41b4d0ce2 · inbound

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A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput

Reference 129

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Unavailable: canonical work link unavailable.

Observation 56ade0ac-c6ae-4299-85e1-3e64c5c9d9e1 · inbound

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AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput

Reference 43

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Observation 067f8b27-9930-4cb9-91ea-6a262abcfd68 · inbound

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems cites this paper.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput

Reference 132

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Observation e937d750-074a-4c97-998a-3aa2b7833d3f · inbound

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A Taxonomy of Performance Metrics for the Distributed Computing Continuum Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput

Reference 82

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