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Paper Citation Record · LEDGER

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems

As of 12 August 2026, this Paper Citation Record lists 100 of 117 outbound references and 0 inbound Pith citation observations for arXiv:2607.02558.

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

pith.paper-citation-record.v1
2607.02558 v1

Coverage vector

measured 100 of 117 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-12T11:05:56.233115Z

measured 100 of 100 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 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

100 of 117 outbound references displayed

  • verified exact6
  • verified fuzzy0
  • unresolved93
  • parse uncertain1
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f200c9ca-de59-40da-a8cc-dfc4fa3a7d2d · outbound

This paper cites and Tumanov, Alexey and Ramjee, Ramachandran , year =.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems and Tumanov, Alexey and Ramjee, Ramachandran , year =

Reference 2

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no resolver link, observed 2026-07-12T11:05:56.233115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:edc4e3ba50f2caa78105b70ab722fd5b9727afa55224ef31cbdefe5bc8561419

Observation d8e821d4-1667-451d-bf7d-e6223cc6fc9d · outbound

This paper cites and Tumanov, Alexey and Ramjee, Ramachandran , year =.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems and Tumanov, Alexey and Ramjee, Ramachandran , year =

Reference 3

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unresolved
no resolver link, observed 2026-07-12T11:05:56.233115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:c59822dfbb5455727dcfdb90a235140921cf05aadd798cdd14c3f39ef6be7257

Observation 46a4171f-798a-4956-81c7-4ab1a21b6a24 · outbound

This paper cites OpenAI Blog , publisher =.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems OpenAI Blog , publisher =

Reference 4

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no resolver link, observed 2026-07-12T11:05:56.233115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:70ee549d66e618de13e4ab721dc97070a437439f9e4e975c3229747f39799533

Observation 07da43f6-9e81-4859-9718-97330ec51864 · outbound

This paper cites 2018 , publisher =.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems 2018 , publisher =

Reference 7

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no resolver link, observed 2026-07-12T11:05:56.233115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:1f405f2d571dd3a1e4ea76605abb0360ff6eceb763afd4b33dd11afe2aadce22

Observation 16e6b657-6e57-4fb7-b893-e1275a3aa098 · outbound

This paper cites Journal of Machine Learning Research , volume =.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Journal of Machine Learning Research , volume =

Reference 11

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no resolver link, observed 2026-07-12T11:05:56.233115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:f467ecc526230ec8b3ca5fbfbf222320f3796bd72e325afe6d979b63f64a8845

Observation 867029bc-65d9-4e3f-b9dd-17834e7f482b · outbound

This paper cites Frans and Morris, Robert , year =.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Frans and Morris, Robert , year =

Reference 12

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unresolved
no resolver link, observed 2026-07-12T11:05:56.233115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:0b7b872ebc33fae2f791c4644c99af9f9e95c02cbc6f3b4ce97c7a71db3b98f0

Observation 9886aafe-8cb1-48ae-9bb8-a358186e2962 · outbound

This paper cites and Ermon, Stefano and Rudra, Atri and R.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems and Ermon, Stefano and Rudra, Atri and R

Reference 14

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no resolver link, observed 2026-07-12T11:05:56.233115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:09bb7419016b30adbfc416a5d86ba29d8855fd63b1ad9ec9f012906d79b9c805

Observation 45e0bb0d-b358-478b-9fae-606d950044d4 · outbound

This paper cites 2012 , journal =.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems 2012 , journal =

Reference 15

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no resolver link, observed 2026-07-12T11:05:56.233115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:299d000e08b3bab4c363a2f47caa9aaec31f60e4df401cf0970b9a860b843e6e

Observation 6613a586-f3db-4390-a0f8-36e78171ad72 · outbound

This paper cites 2022 , booktitle =.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems 2022 , booktitle =

Reference 18

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no resolver link, observed 2026-07-12T11:05:56.233115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:c168ac8d31df2d21107de725c36c779a98f27f79d1511e4fe789926f7d72fb08

Observation 1224bd4d-92d2-43e6-927f-e0138caadfc9 · outbound

This paper cites Proceedings of the 12th International Conference on Learning Representations (ICLR) , publisher =.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Proceedings of the 12th International Conference on Learning Representations (ICLR) , publisher =

Reference 19

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no resolver link, observed 2026-07-12T11:05:56.233115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:df0066d5183aa329a849bfd872b9335b0ab999c79a02599f831c0973dd39798c

Observation 25beb876-9a25-452a-8ddc-b165ba8f1d04 · outbound

This paper cites Proceedings of the 11th International Conference on Learning Representations (ICLR) , publisher =.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Proceedings of the 11th International Conference on Learning Representations (ICLR) , publisher =

Reference 20

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no resolver link, observed 2026-07-12T11:05:56.233115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:f039537fde146907e46544b2ac7c327d40437cda6977832b7ff5d278909fafb6

Observation e926c300-4ef7-4963-af16-df363ad0d145 · outbound

This paper cites 2021 , journal =.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems 2021 , journal =

Reference 21

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no resolver link, observed 2026-07-12T11:05:56.233115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:cd737dc3593934d14a5cae627c96780d091f17d1612e61814d6da6a1c5786799

Observation 7d036c83-54b2-4c99-b280-58046baeb20c · outbound

This paper cites and Brooks, David and Wu, Carole-Jean , year =.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems and Brooks, David and Wu, Carole-Jean , year =

Reference 22

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no resolver link, observed 2026-07-12T11:05:56.233115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:f34d43ab335e4c2e768cdb0913a3279f27f4c9ab0ffecd16dd5ba1aaf073626e

Observation 861e67e5-c075-45b1-940e-65dba10798ee · outbound

This paper cites 2022 , booktitle =.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems 2022 , booktitle =

Reference 23

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no resolver link, observed 2026-07-12T11:05:56.233115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:eb9a10598da3b105141e72131dd3ce45886236782c8990f96d91df12720f9db2

Observation 12eaf00a-c296-4e58-9591-683220d1f632 · outbound

This paper cites 2016 , booktitle =.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems 2016 , booktitle =

Reference 24

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no resolver link, observed 2026-07-12T11:05:56.233115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:494335509119523b16a60aa716507cc95d34f507ca90a2425dc791ffc9fc9d10

Observation f1d28c34-8e2f-48fc-b294-c3f9b104005a · outbound

This paper cites 2024 , publisher =.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems 2024 , publisher =

Reference 25

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no resolver link, observed 2026-07-12T11:05:56.233115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:ba9a2fc886d216f33ef7c66877df91612906559f33ec15eac621faed36990740

Observation 0ed6b0da-e52f-4155-9e76-3c52f034229c · outbound

This paper cites 2022 , booktitle =.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems 2022 , booktitle =

Reference 26

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:fc7ebe944194d1d57decfdf4bb01ccdc1af48c16844a0380073f3c9942ed6eaa

Observation 07f7dd8c-6f56-4c5f-8ee9-246f8d9069ca · outbound

This paper cites 2019 , booktitle =.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems 2019 , booktitle =

Reference 27

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no resolver link, observed 2026-07-12T11:05:56.233115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:e5aaae0dffe0fff3c58a71dd6e104451f878af9748d71436c9052e694ba3905b

Observation 409c5646-189a-4218-b7ad-f9d12d064b5a · outbound

This paper cites 2017 , booktitle =.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems 2017 , booktitle =

Reference 28

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no resolver link, observed 2026-07-12T11:05:56.233115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:6d96071a7e51ed3f8acedbaf44c74930a941d5b66ab93c559a9e46050d80d2a5

Observation bc7f51a7-4765-4e58-ae03-5e422c045dc4 · outbound

This paper cites 2020 , journal =.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems 2020 , journal =

Reference 29

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no resolver link, observed 2026-07-12T11:05:56.233115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:73aaac463b2a10ad1c98edbc6bf268682f12f68da3f2cd74f356d6cfb3632a82

Observation a8c974df-3c72-4627-850f-a4320459e343 · outbound

This paper cites 2023 , note =.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems 2023 , note =

Reference 30

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source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:e23794801525581d677062eaf867032f1aa7616e780bf79606420d20cdf9778f

Observation ebfdd119-2b38-4689-8efa-05880384a9fe · outbound

This paper cites 1985 , journal =.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems 1985 , journal =

Reference 32

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no resolver link, observed 2026-07-12T11:05:56.233115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:dac76febf964b861eacee47e6b94a86e0c8a6c55f61eab553f15c51ff86b1a91

Observation ef444520-2b33-404e-a57b-7217c0a513b1 · outbound

This paper cites 2023 , booktitle =.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems 2023 , booktitle =

Reference 33

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no resolver link, observed 2026-07-12T11:05:56.233115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:a27ffc5c1267e87d127bc10b47eca5651260b6c22b33afa9e74f9e80bd9f4d12

Observation 6bdd0854-4ddf-43d1-af2c-6b4958665eed · outbound

This paper cites Lumos: Efficient Performance Modeling and Estimation for Large-scale.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Lumos: Efficient Performance Modeling and Estimation for Large-scale

Reference 34

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:1620aa7bdd13bc5969471ec83d2fa45ea9885ae8984182f98492a8098c021d3c

Observation b0ac2187-5237-418b-ac85-f73ae5079eb7 · outbound

This paper cites Cerebras Architecture Deep Dive: First Look Inside the.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Cerebras Architecture Deep Dive: First Look Inside the

Reference 35

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source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:72f85af01368ced396f172d4cd1a91d6def9d1be209d1bb7463f83b50b3a34e2

Observation b56c9a82-1e56-4053-b78f-4d6eef7138dc · outbound

This paper cites Proceedings of Machine Learning and Systems (MLSys) , publisher =.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Proceedings of Machine Learning and Systems (MLSys) , publisher =

Reference 36

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source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:191e48c5c95aa0fe02c24ba975b7662ac27757aae395b292168d8a9e89867a78

Observation a25a45ff-90a9-47ca-855c-5639a0cddacf · outbound

This paper cites 2024 , journal =.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems 2024 , journal =

Reference 38

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parse uncertain
no resolver link, observed 2026-07-12T11:05:56.233115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:9a8308e4de3901a3da4305f8a9e19078c2b8d687878516c8a3291a6c2457924c

Observation e437281b-9a52-410e-a631-624d9fc1a400 · outbound

This paper cites 2019 , howpublished =.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems 2019 , howpublished =

Reference 39

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source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:e5332e4115853a35303dbc4f1b954dbd3313b766aab03a1c203cf1d21865e6d4

Observation 09cbdb43-815a-414d-a6c6-08973f064eeb · outbound

This paper cites 2025 , publisher =.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems 2025 , publisher =

Reference 41

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:9c6dcc51f4fa3f5188b53f61419d78bc0017cd13b45187272ad20a61760ab1b9

Observation e1a9446d-ee77-40c6-bc70-ca2e6e258ed6 · outbound

This paper cites Analyzing and Mitigating Data Stalls in.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Analyzing and Mitigating Data Stalls in

Reference 42

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:40dcb6ad591f854aa1bee89589ca9174a25c8c3746d8d294e6d4400c9d283353

Observation 33ebd170-63fe-4d2d-b575-6fe304265645 · outbound

This paper cites 2021 , booktitle =.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems 2021 , booktitle =

Reference 43

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:62c826c3ab9290847ddf77a4e7db5c9d95cf40decefbe540f60b6813d325ddc4

Observation a737c0be-2544-4076-9360-e7332904fbdd · outbound

This paper cites 2021 , journal =.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems 2021 , journal =

Reference 44

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:da051c34b1e060e177d91f08542070831aee49e980edc00b43d27a4573dcad13

Observation 4dac24b4-67b9-4139-9d86-c573ed178366 · outbound

This paper cites 2014 , publisher =.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems 2014 , publisher =

Reference 47

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source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:3f21c904aac508beac719943c4d493de4114700850f8ce496624f1afa5a2654c

Observation ea21f0d1-e176-4c29-8bb3-6ba037544f2f · outbound

This paper cites 2021 , journal =.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems 2021 , journal =

Reference 48

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source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:80932b94df983a4259a52508399b18b73d0738dc8ca0709736694ec5330cfbc6

Observation 2ef04bed-e58b-42d9-b35a-0dfabd621ce5 · outbound

This paper cites 2023 , journal =.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems 2023 , journal =

Reference 49

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source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:233dc8d53f276ce73d19de31874a856ea66ba02398bb71a5a145d7d33d5b1839

Observation f3f61af8-c870-400a-9688-1c6df84f99d3 · outbound

This paper cites and Talwalkar, Ameet , year =.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems and Talwalkar, Ameet , year =

Reference 50

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source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:65c6347201c0bcadb8b8a27725250be02e7c28d90cdf46d2fda2e7eb4d311b9b

Observation eb42df78-332f-4580-b69d-ec9f24f8e558 · outbound

This paper cites ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , publisher =.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , publisher =

Reference 52

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source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:769ecf206ce13f0b8a464641eb0cc46fdbcc1c834866de3fae5f4d2119c20fd0

Observation a81fe4c3-9513-4f33-9189-f9e77e3c0823 · outbound

This paper cites 2017 , booktitle =.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems 2017 , booktitle =

Reference 53

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source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:750c6558289b65c73c3930282cf90ab32d54d6b753f6a035e6d3672a0c7040dd

Observation dd730cee-f680-4407-bb6e-805ef2a72c90 · outbound

This paper cites an unresolved cited work.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Unresolved cited work

Reference 55

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source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:b370e503b71798f2a45f20260353025d2268eb26af2451d90f1f7e99e7c2d872

Observation a9729f4f-8c17-463b-ae0d-849fb3fb278c · outbound

This paper cites and LaPiana, Lia S.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems and LaPiana, Lia S

Reference 56

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source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:a0e6339feeaecf7607204688ef0310ada8ccd58329c9c07e4bef07ddc295edbb

Observation 7e332eaa-4a21-4312-a4e6-a920facb6c72 · outbound

This paper cites 2019 , note =.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems 2019 , note =

Reference 57

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Observation cb5a9d2c-c474-4b31-b3ce-47d6fc27013e · outbound

This paper cites 2006 , publisher =.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems 2006 , publisher =

Reference 58

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Observation 2777d7f0-2d54-4e53-8fe4-4cfc20acf737 · outbound

This paper cites an unresolved cited work.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Unresolved cited work

Reference 59

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Observation f15bbf22-8af3-40f8-936c-2eaba07ad776 · outbound

This paper cites Proceedings of the 22nd USENIX Symposium on Networked Systems Design and Implementation (NSDI) , publisher =.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Proceedings of the 22nd USENIX Symposium on Networked Systems Design and Implementation (NSDI) , publisher =

Reference 60

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Observation cce96694-1edb-4990-addb-51fb130c1333 · outbound

This paper cites Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale

Reference 63

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Observation 1863513d-675e-4afd-bda4-304819f63296 · outbound

This paper cites 2021 , booktitle =.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems 2021 , booktitle =

Reference 66

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Observation 2f56283a-de44-4c79-811a-022269d6b8dc · outbound

This paper cites SGLang: Efficient Execution of Structured Language Model Programs.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems SGLang: Efficient Execution of Structured Language Model Programs

Reference 69

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Observation 61f429ac-556f-4a53-ba12-8710e0a943ca · outbound

This paper cites Proceedings of the 18th USENIX Symposium on Operating Systems Design and Implementation (OSDI) , publisher =.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Proceedings of the 18th USENIX Symposium on Operating Systems Design and Implementation (OSDI) , publisher =

Reference 70

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Observation 688ff359-967e-4b95-8782-3f866e363de2 · outbound

This paper cites Deep learning with differential privacy.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Deep learning with differential privacy

Reference 71

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Observation b7c30e98-6224-4cf5-895b-8160f434681e · outbound

This paper cites Taming Throughput-Latency tradeoff in LLM inference with Sarathi-Serve.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Taming Throughput-Latency tradeoff in LLM inference with Sarathi-Serve

Reference 72

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Observation 26d67925-bb66-423d-a69f-9b4917a700de · outbound

This paper cites Vidur: A Large-Scale Simulation Framework For LLM Inference.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Vidur: A Large-Scale Simulation Framework For LLM Inference

Reference 73

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Observation f1cfe1ea-0cbd-4e32-ab63-b576b25c3c1e · outbound

This paper cites Demystifying AI Platform Design for Distributed Inference of Next-Generation LLM models.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Demystifying AI Platform Design for Distributed Inference of Next-Generation LLM models

Reference 74

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Observation 71011312-dfe1-41df-b618-68b669f4462b · outbound

This paper cites The case for energy-proportional computing.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems The case for energy-proportional computing

Reference 75

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Observation c4b36731-26f6-4fd0-a7ff-268c7df2f454 · outbound

This paper cites The Datacenter as a Computer.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems The Datacenter as a Computer

Reference 76

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Observation b509463e-860b-4e45-8bb3-b191460cc73e · outbound

This paper cites The gem5 simulator.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems The gem5 simulator

Reference 77

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Observation b5722d71-ef75-4cc7-ae4d-7878f96d8d8a · outbound

This paper cites an unresolved cited work.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Unresolved cited work

Reference 78

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source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:0f91a8b60f17ea6a619d06d7764a0bd6e791c3c2c861e857c6d90a74f62d0539

Observation 5f47130f-0fed-4ba4-91c4-70c58ebe06ef · outbound

This paper cites PaLM : Scaling language modeling with pathways.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems PaLM : Scaling language modeling with pathways

Reference 79

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Observation 7c9a465f-8e3d-47bf-8d8a-855a618478d7 · outbound

This paper cites Frans Kaashoek, and Robert Morris.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Frans Kaashoek, and Robert Morris

Reference 80

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Observation 621d1842-402e-43aa-ad5f-0d60b8d02673 · outbound

This paper cites an unresolved cited work.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Unresolved cited work

Reference 81

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Observation 9016818c-bf4a-444b-b394-f081c5a448b3 · outbound

This paper cites Fu, Stefano Ermon, Atri Rudra, and Christopher R \'e.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Fu, Stefano Ermon, Atri Rudra, and Christopher R \'e

Reference 82

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Observation 432b966e-eb26-4a22-a86e-3ca823ec7e42 · outbound

This paper cites The tail at scale.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems The tail at scale

Reference 83

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Observation e1e85d24-dc1b-42a7-8e21-bd5109fef5dc · outbound

This paper cites Corrado, Rajat Monga, et al.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Corrado, Rajat Monga, et al

Reference 84

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Observation 29454e09-bbc3-4c99-a80d-35b058885278 · outbound

This paper cites Insights into deepseek-v3: Scaling challenges and reflections on hardware for ai architectures.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Insights into deepseek-v3: Scaling challenges and reflections on hardware for ai architectures

Reference 85

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Observation 157ab128-b7f9-41b4-8a02-b56d404c3792 · outbound

This paper cites Check-n-run: a checkpointing system for training deep learning recommendation models.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Check-n-run: a checkpointing system for training deep learning recommendation models

Reference 86

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Observation e28ae6eb-e398-4497-84b9-f029ef2527ce · outbound

This paper cites LLMCarbon : Modeling the end-to-end carbon footprint of large language models.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems LLMCarbon : Modeling the end-to-end carbon footprint of large language models

Reference 87

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Observation c0276eca-3129-4f9f-b1df-d3e6fb1108ed · outbound

This paper cites Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity

Reference 88

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Observation fe10ba89-c4d1-412c-abb2-b87ff1dc64c2 · outbound

This paper cites GPTQ : Accurate post-training quantization for generative pre-trained transformers.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems GPTQ : Accurate post-training quantization for generative pre-trained transformers

Reference 89

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source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:8782b6820dfb8f551fe98426eddb542d8f5c8f73e1823fb35cf5d27bfdbd2f9b

Observation 2ac38744-0eff-4a60-b614-9027b9e81c94 · outbound

This paper cites A Survey of Quantization Methods for Efficient Neural Network Inference, pages 291--326.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems A Survey of Quantization Methods for Efficient Neural Network Inference, pages 291--326

Reference 90

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Observation 15458bac-8ecd-4643-bee6-647b4471e4fd · outbound

This paper cites Chasing carbon: The elusive environmental footprint of computing.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Chasing carbon: The elusive environmental footprint of computing

Reference 91

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Observation 75b8af9b-6c50-40f7-81b8-fe9a14f477ac · outbound

This paper cites an unresolved cited work.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Unresolved cited work

Reference 92

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Observation e12217a8-5ca0-4706-91aa-d9035242c979 · outbound

This paper cites Hennessy, David A.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Hennessy, David A

Reference 93

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source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:8a65ba0cc64a3c951d7dd8b70202aea8c1701044dca816dab3c8634154d09acc

Observation 2d8a75c0-9982-476e-a321-c30a5f0ff587 · outbound

This paper cites Training compute-optimal large language models.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Training compute-optimal large language models

Reference 94

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Observation e97b53d9-abf7-4a69-8518-46b7b025ad33 · outbound

This paper cites Calculon: A methodology and tool for high-level co-design of systems and large language models.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Calculon: A methodology and tool for high-level co-design of systems and large language models

Reference 95

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source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:d42e0f6046941472afcc47d3e9a520e1abbc53cf654a6e1bd5f26eb21a6e78a0

Observation d303bbc3-95cd-4124-b2d5-4a4351699208 · outbound

This paper cites Beyond Data and Model Parallelism for Deep Neural Networks.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Beyond Data and Model Parallelism for Deep Neural Networks

Reference 96

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source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:d23486e2f3b2928cd1aa3fcef93f000ca545cacbe5262d09d9e80a32d7bb4116

Observation d7abc836-ee32-4239-8a2c-5877a4ef7394 · outbound

This paper cites Reducing activation recomputation in large transformer models.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Reducing activation recomputation in large transformer models

Reference 97

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Observation 3fb980d7-08b9-4d7e-9615-8cf7cac0fd54 · outbound

This paper cites Efficient memory management for large language model serving with pagedattention.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Efficient memory management for large language model serving with pagedattention

Reference 98

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Observation 6b294f78-db47-4e00-a1f5-182bed56d9e7 · outbound

This paper cites Leiserson.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Leiserson

Reference 99

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source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:2c3c71c3b0eecb211d6edd430f285ca07dbf920e306b73e97577e91019d5be58

Observation 48630c95-b8c4-4def-acbd-206a64997de2 · outbound

This paper cites GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

Reference 100

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source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:76ff57bcd8b17a4a11ae435ec32de70d4201a2986af4282dd2dc13002699aad3

Observation 0fb7acb4-4e56-44d6-b13c-2351e3a9bb3e · outbound

This paper cites Fast inference from transformers via speculative decoding.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Fast inference from transformers via speculative decoding

Reference 101

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Observation fcf1b673-55bb-4d00-ab84-f87587f30f29 · outbound

This paper cites llm-analysis: Latency and memory analysis of transformer models.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems llm-analysis: Latency and memory analysis of transformer models

Reference 102

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Observation e8a33236-8749-4d13-b576-c1d2e43a2225 · outbound

This paper cites Lumos: Efficient performance modeling and estimation for large-scale LLM training.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Lumos: Efficient performance modeling and estimation for large-scale LLM training

Reference 103

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source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:dc7ff0df82cb6a946184af9bc6f695c14c5b3cbbb5b83f6e4434f79ec89b8ce3

Observation 4967e7bb-7e2b-4ee0-936f-3db054c305cd · outbound

This paper cites Cerebras architecture deep dive: First look inside the HW/SW co-design for deep learning.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Cerebras architecture deep dive: First look inside the HW/SW co-design for deep learning

Reference 104

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source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:4fb460da27dba1975e36aaf78ec6c7d6ae4b3d8f9fc6367786452d7978ebd2a2

Observation 3f69ae97-92a7-4d2f-b86b-94f08592f3d9 · outbound

This paper cites Awq: Activation-aware weight quantization for on-device llm compression and acceleration.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Awq: Activation-aware weight quantization for on-device llm compression and acceleration

Reference 105

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source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:f0d788ee959e71bb4729a71a410b55b800a1db7f13a3a359ff18b49c1f2b4e91

Observation 472de1af-1b84-4081-bbdb-024b7420455b · outbound

This paper cites an unresolved cited work.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Unresolved cited work

Reference 106

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source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:e465ddabfcb9d7f241e4834039e9b14121fc10a736b5001cc4f531bbd97372bb

Observation 0b3f449d-7a37-40b9-9617-2f3c3ff3f2a7 · outbound

This paper cites The Llama 3 Herd of Models.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems The Llama 3 Herd of Models

Reference 107

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source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:1a3ee06383a54f7ce785861c005365ebc1b050fd11e7b0ebbb4c799da6e6ce0c

Observation 95e6ab5e-8cb3-49f5-b6d7-7a543d3175ca · outbound

This paper cites Energy Usage Reports: Environmental awareness as part of algorithmic accountability.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Energy Usage Reports: Environmental awareness as part of algorithmic accountability

Reference 108

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source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:3c71e3f2bbce46fc920679cea102628c837e999c0cae23b80deab1799d4985ca

Observation 8338d54e-f269-44c8-b3b3-f63ef3968963 · outbound

This paper cites Mlperf: An industry standard benchmark suite for machine learning performance.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Mlperf: An industry standard benchmark suite for machine learning performance

Reference 109

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verified exact
arxiv_id, observed 2026-07-12T11:08:51.854731Z

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.

source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:28de9df83255a6158be5d754ac2e116f15523ff91523d30144cc0247cedeea01

Observation f00b0b8e-1d68-4aad-9d97-fc61ea22dc24 · outbound

This paper cites Analyzing and mitigating data stalls in dnn training.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Analyzing and mitigating data stalls in dnn training

Reference 110

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arxiv_id, observed 2026-07-12T11:08:51.978200Z

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.

source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:ebedb8109958b128c43ebc9a9050771a28d41535abcb908e2a2403c810751d09

Observation 09538eec-dc11-4a1d-82e2-07e420cf0a64 · outbound

This paper cites Murray, Jiri Simsa, Ana Klimovic, and Ihor Indyk.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Murray, Jiri Simsa, Ana Klimovic, and Ihor Indyk

Reference 111

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source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:53d0201355df1554da65a620ef6754423922ce9a65b0f06a4481019939053efc

Observation e774cc40-177c-4f42-878b-f73630f39467 · outbound

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

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Efficient large-scale language model training on gpu clusters using megatron-lm

Reference 112

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source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:1592ee5b1141da470a264135f76612c929355e3ce69151ae98936540b6de95ca

Observation 0c86536b-f3c0-4b05-99d8-a93f746d6b62 · outbound

This paper cites NVIDIA H100 Tensor Core GPU datasheet.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems NVIDIA H100 Tensor Core GPU datasheet

Reference 113

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source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:f8e0a865058b4dddf1106b80fad8cc16f5bba79ba84fd590cb800d53f19aa0af

Observation 3871e21c-94a6-442e-b066-3cf356255fd5 · outbound

This paper cites Timeloop: A systematic approach to dnn accelerator evaluation.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Timeloop: A systematic approach to dnn accelerator evaluation

Reference 114

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source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:cf05a745702aafb1e95b97f32c4fe202db463e8d23663243995bbb6081e2d382

Observation 788fb815-a35d-4e0e-93b4-7ef277cf0e7e · outbound

This paper cites Splitwise: Efficient generative llm inference using phase splitting.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Splitwise: Efficient generative llm inference using phase splitting

Reference 115

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source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:72ffc9fccf9431d7fffdc6336f23807d26128f61afa7869ee288e619131a19c0

Observation 01f2d315-3b17-45ce-9dfe-bc71fcb0c579 · outbound

This paper cites Carbon Emissions and Large Neural Network Training.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Carbon Emissions and Large Neural Network Training

Reference 116

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source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:3549617f950ec26aa436497f9cfa32da35ae558e78c88b6c59d12f2df2e00dc4

Observation 82f65f51-e0df-44d4-84c9-940949f28834 · outbound

This paper cites Patterson and John L.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Patterson and John L

Reference 117

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source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:a0bf41ea92d32f5292df6b77004fb5f98831ae52ced7849e02b82c8ae5124a41

Observation 5ddb7890-a158-4b7c-87cc-97a49d00c9e4 · outbound

This paper cites Efficiently scaling transformer inference.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Efficiently scaling transformer inference

Reference 118

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source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:4408add7259ea373d79411e1cffb839629f305fad111325cb78d72800dc7d90d

Observation 50108b81-8993-4b1e-8731-b9d2c1964cb9 · outbound

This paper cites Sparks, and Ameet Talwalkar.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Sparks, and Ameet Talwalkar

Reference 119

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source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:03746fe5052ba6aa979bf59ace80875068728255420c5aad3731b09824480dd9

Observation 5b063496-79f3-4be8-b37c-028c671c3e10 · outbound

This paper cites Generalized Slow Roll for Tensors.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Generalized Slow Roll for Tensors

Reference 120

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source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:fcdb91ff11ad0182551a055d35809fd6edbfc2f640ec73c1d680653880bd4cc1

Observation e9c57b70-0091-4aeb-8e84-13bfebbb1a16 · outbound

This paper cites Machine Learning Systems: Principles and Practices of Engineering Artificially Intelligent Systems.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems Machine Learning Systems: Principles and Practices of Engineering Artificially Intelligent Systems

Reference 121

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source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:def58aa178efef6bf259491f58c0ec8b812af5b532e898ecb65c13baa022eef5

Observation 3dfc6296-0a59-4acb-9d19-c25133ae8ba8 · outbound

This paper cites TinyTorch : A progressive educational framework for machine learning systems.

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems TinyTorch : A progressive educational framework for machine learning systems

Reference 122

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source=arxiv_source observed=2026-07-12T11:05:56.233115Z digest=sha256:1d40cc60e549b38b34ecb326195a208ab245378aad18dec75f8489923b846dcf

Pith citing papers

No inbound Pith citation observations are available.