Paper Citation Record · LEDGER

Demystifying the MLPerf Benchmark Suite

As of 15 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:1908.09207.

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

pith.paper-citation-record.v1
1908.09207 v1

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T11:21:53.434636Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

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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

50 of 50 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5f9d29f2-5438-4bd9-b12c-a83e4c5c798e · outbound

This paper cites TensorFlow: Large-scale machine learning on heterogeneous systems,.

Demystifying the MLPerf Benchmark Suite TensorFlow: Large-scale machine learning on heterogeneous systems,

Reference 1

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Observation 365455be-26e0-4a56-9060-2d188c0dfb1d · outbound

This paper cites Fathom: Reference workloads for modern deep learning methods,.

Demystifying the MLPerf Benchmark Suite Fathom: Reference workloads for modern deep learning methods,

Reference 2

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Observation c2b7baae-e8af-4016-91a7-b0b2bb6260a2 · outbound

This paper cites Deepbench: Benchmarking deep learning operations on different hardware,.

Demystifying the MLPerf Benchmark Suite Deepbench: Benchmarking deep learning operations on different hardware,

Reference 3

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Observation f6b9bffa-64b8-4750-83ad-b8d336b01815 · outbound

This paper cites An update to deepbench with a focus on deep learning inference,.

Demystifying the MLPerf Benchmark Suite An update to deepbench with a focus on deep learning inference,

Reference 4

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Observation 440a229d-b883-4776-8fee-d73978474c67 · outbound

This paper cites Resnet18 + minor modiﬁcations (submission at DAWNBench),.

Demystifying the MLPerf Benchmark Suite Resnet18 + minor modiﬁcations (submission at DAWNBench),

Reference 5

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Observation f95a9c18-67d2-4566-9c9d-de10aaea91cc · outbound

This paper cites Findings of the 2017 conference on machine translation (wmt17),.

Demystifying the MLPerf Benchmark Suite Findings of the 2017 conference on machine translation (wmt17),

Reference 6

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Observation b35cc26d-8055-4f20-aafc-f548812f21a4 · outbound

This paper cites MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems.

Demystifying the MLPerf Benchmark Suite MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems

Reference 7

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Observation 1e77528b-5409-4fbf-9597-104755d19901 · outbound

This paper cites cudnn: Efﬁ- cient primitives for deep learning,.

Demystifying the MLPerf Benchmark Suite cudnn: Efﬁ- cient primitives for deep learning,

Reference 8

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Observation 9343d428-f965-48ef-bb54-c0e327652011 · outbound

This paper cites Dawnbench : An end-to-end deep learning benchmark and competition,.

Demystifying the MLPerf Benchmark Suite Dawnbench : An end-to-end deep learning benchmark and competition,

Reference 9

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Observation a3fe1f58-8e85-4a1f-be5a-a412c006d4cb · outbound

This paper cites Imagenet: A large-scale hierarchical image database,.

Demystifying the MLPerf Benchmark Suite Imagenet: A large-scale hierarchical image database,

Reference 10

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Observation c3685c70-166d-4974-b97a-2f61c773f77d · outbound

This paper cites White paper fujitsu server primergy & primequest memory performance of xeon scalable processor(skylake-sp) based systems,.

Demystifying the MLPerf Benchmark Suite White paper fujitsu server primergy & primequest memory performance of xeon scalable processor(skylake-sp) based systems,

Reference 11

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Observation e2646b35-ca5c-46b5-b15e-c271fc074c03 · outbound

This paper cites MovieLens,.

Demystifying the MLPerf Benchmark Suite MovieLens,

Reference 12

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Observation 179bdbe7-fa1b-4820-8fb2-c651f6e2712e · outbound

This paper cites Deep learning with limited numerical pre- cision,.

Demystifying the MLPerf Benchmark Suite Deep learning with limited numerical pre- cision,

Reference 13

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Observation fb263901-f4d3-4a24-8c20-afcf034a2bd4 · outbound

This paper cites Ai benchmarks remain immature,.

Demystifying the MLPerf Benchmark Suite Ai benchmarks remain immature,

Reference 14

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Observation b9696d51-f0cc-4853-b84a-38ab96f09c92 · outbound

This paper cites The movielens datasets: History and context,.

Demystifying the MLPerf Benchmark Suite The movielens datasets: History and context,

Reference 15

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Source-reported events for the cited work

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Observation f45463bc-d357-4c53-a904-8eba94c5d09f · outbound

This paper cites Mask r-cnn,.

Demystifying the MLPerf Benchmark Suite Mask r-cnn,

Reference 16

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Observation 87638781-4ace-4662-9b18-0aa643b2b2e8 · outbound

This paper cites Deep residual learn- ing for image recognition,.

Demystifying the MLPerf Benchmark Suite Deep residual learn- ing for image recognition,

Reference 17

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Observation e690c560-9e68-412e-97cc-34c32c3c69f7 · outbound

This paper cites Identity mappings in deep residual networks,.

Demystifying the MLPerf Benchmark Suite Identity mappings in deep residual networks,

Reference 18

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Observation 79091b05-f34e-44df-93a3-fe25c98bbb06 · outbound

This paper cites Neural collaborative ﬁltering,.

Demystifying the MLPerf Benchmark Suite Neural collaborative ﬁltering,

Reference 19

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Observation d3abf1e2-37f2-4318-9ff5-ee8488c0ee83 · outbound

This paper cites Quantized neural networks: Training neu- ral networks with low precision weights and activations,.

Demystifying the MLPerf Benchmark Suite Quantized neural networks: Training neu- ral networks with low precision weights and activations,

Reference 20

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Observation c4cf6fef-ebcc-4cf9-a97d-37361d609dbf · outbound

This paper cites Learning multiple layers of features f rom tiny images,.

Demystifying the MLPerf Benchmark Suite Learning multiple layers of features f rom tiny images,

Reference 21

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Observation 5ef7d594-434c-4520-bf21-7add5a844233 · outbound

This paper cites Microsoft coco: Common objects in context,.

Demystifying the MLPerf Benchmark Suite Microsoft coco: Common objects in context,

Reference 22

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Observation 1083f947-f332-4761-8507-cefdbce79e4b · outbound

This paper cites Ssd: Single shot multibox detector,.

Demystifying the MLPerf Benchmark Suite Ssd: Single shot multibox detector,

Reference 23

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Observation d9f93952-acbf-4e3b-8db0-4a2f09f21179 · outbound

This paper cites Mlperf design challenges,.

Demystifying the MLPerf Benchmark Suite Mlperf design challenges,

Reference 24

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This paper cites Mixed precision training,.

Demystifying the MLPerf Benchmark Suite Mixed precision training,

Reference 25

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Observation 48e862bd-495a-40b7-a5b1-e95fc147794f · outbound

This paper cites Performance characteristics of common transports and buses,.

Demystifying the MLPerf Benchmark Suite Performance characteristics of common transports and buses,

Reference 26

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Observation 94a53811-542e-44e1-a18c-1a4dbcbd8b83 · outbound

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Demystifying the MLPerf Benchmark Suite Unresolved cited work

Reference 27

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This paper cites Nvidia collective communications library (nccl),.

Demystifying the MLPerf Benchmark Suite Nvidia collective communications library (nccl),

Reference 28

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Observation ba7fcf46-dc2b-4ff8-90fa-335317a1e658 · outbound

This paper cites Nvidia tesla v100 gpu accelerator,.

Demystifying the MLPerf Benchmark Suite Nvidia tesla v100 gpu accelerator,

Reference 29

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Observation 64a8bbe0-bfba-4285-969e-e8c85504faf9 · outbound

This paper cites Automatic mixed precision (amp),.

Demystifying the MLPerf Benchmark Suite Automatic mixed precision (amp),

Reference 30

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Demystifying the MLPerf Benchmark Suite Nvidia system management in- terface program,

Reference 31

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This paper cites Automatic differentiation in pytorch,.

Demystifying the MLPerf Benchmark Suite Automatic differentiation in pytorch,

Reference 32

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This paper cites Squad : 100,000+ questions for machine comprehension of text,.

Demystifying the MLPerf Benchmark Suite Squad : 100,000+ questions for machine comprehension of text,

Reference 33

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Observation d70ce0cb-2c10-4310-81be-796b6ed86618 · outbound

This paper cites Mastering the game of go with deep neural networks and tree search,.

Demystifying the MLPerf Benchmark Suite Mastering the game of go with deep neural networks and tree search,

Reference 34

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This paper cites Mastering chess and shogi by self-play with a general re- inforcement learning algorithm,.

Demystifying the MLPerf Benchmark Suite Mastering chess and shogi by self-play with a general re- inforcement learning algorithm,

Reference 35

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This paper cites Mas- tering the game of go without human knowledge,.

Demystifying the MLPerf Benchmark Suite Mas- tering the game of go without human knowledge,

Reference 36

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Observation a1995b30-455c-4a6b-b5e9-dffca6b94369 · outbound

This paper cites Minigo: A minimalist Go engine modeled after AlphaGo Zero, built on MuGo,.

Demystifying the MLPerf Benchmark Suite Minigo: A minimalist Go engine modeled after AlphaGo Zero, built on MuGo,

Reference 37

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This paper cites XLA (accelerated linear algebra),.

Demystifying the MLPerf Benchmark Suite XLA (accelerated linear algebra),

Reference 38

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This paper cites Iostat: I/o statistics tool,.

Demystifying the MLPerf Benchmark Suite Iostat: I/o statistics tool,

Reference 39

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This paper cites Netstat: Network status and statistics tool,.

Demystifying the MLPerf Benchmark Suite Netstat: Network status and statistics tool,

Reference 40

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Demystifying the MLPerf Benchmark Suite Vmstat: Virtual memory statistics tool,

Reference 41

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Observation 9c98fb84-1237-431f-a42f-6ac282eb1ff4 · outbound

This paper cites Attention is all you need,.

Demystifying the MLPerf Benchmark Suite Attention is all you need,

Reference 42

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Demystifying the MLPerf Benchmark Suite Nvidia gpu utilization plugin for dstat ,

Reference 43

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This paper cites Dstat: V ersatile resource statistics too l,.

Demystifying the MLPerf Benchmark Suite Dstat: V ersatile resource statistics too l,

Reference 44

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Observation 391bd7c2-dafe-4c6f-979d-aaaac4266a48 · outbound

This paper cites Rooﬂine: An insightful visual performance model for multicore architectures,.

Demystifying the MLPerf Benchmark Suite Rooﬂine: An insightful visual performance model for multicore architectures,

Reference 45

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Observation ff4149a8-ec3e-4869-a75a-cc17ba63e5c7 · outbound

This paper cites Google’s neural machine translation system: Bridging the gap between human and machine transla- tion,.

Demystifying the MLPerf Benchmark Suite Google’s neural machine translation system: Bridging the gap between human and machine transla- tion,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:21:53.623234Z

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Observation 36110249-d5e8-41e4-b436-e78e294e8dbb · outbound

This paper cites Berkeley cs rooﬂine toolkit,.

Demystifying the MLPerf Benchmark Suite Berkeley cs rooﬂine toolkit,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:21:53.608469Z

Source-reported events for the cited work

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Observation 7e137cf6-fd7e-45b9-98e4-5af392e16e5c · outbound

This paper cites DrQA (sub- mission at DAWNBench),.

Demystifying the MLPerf Benchmark Suite DrQA (sub- mission at DAWNBench),

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:21:53.594454Z

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Observation 5620e462-d7ad-4b5b-9bee-d0f985cdce51 · outbound

This paper cites Why machine learning needs benchmarks,.

Demystifying the MLPerf Benchmark Suite Why machine learning needs benchmarks,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:21:53.581520Z

Source-reported events for the cited work

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Observation d29eca45-d07b-4079-bf7c-7a7660fa9511 · outbound

This paper cites Tbd: Bench- marking and analyzing deep neural network training,.

Demystifying the MLPerf Benchmark Suite Tbd: Bench- marking and analyzing deep neural network training,

Reference 50

Resolution: malformed identifier; raw_fallback, observed 2026-08-14T11:21:53.565603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

No inbound Pith citation observations are available.