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Paper Citation Record · LEDGER

Dissecting the NVidia Turing T4 GPU via Microbenchmarking

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:1903.07486.

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

pith.paper-citation-record.v1
1903.07486 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:33:37.932535Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-03T14:08:21.715092Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 7480e80d-577f-4611-8e4f-2edee0f33995 · inbound

CuAsmRL: Optimizing GPU SASS Schedules via Deep Reinforcement Learning cites this paper.

CuAsmRL: Optimizing GPU SASS Schedules via Deep Reinforcement Learning Dissecting the NVidia Turing T4 GPU via Microbenchmarking

Reference 2019

Resolution
unresolved
no resolver link, observed 2026-08-10T20:33:37.932535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:33:37.932535Z digest=sha256:36c9536c6080a1b0a6923605b480701d313ec486e78abb8026340d80251da5b1

Observation 44af9cb7-98dc-4988-a4a5-ee6d180a171d · inbound

Dissecting the NVIDIA Hopper Architecture through Microbenchmarking and Multiple Level Analysis cites this paper.

Dissecting the NVIDIA Hopper Architecture through Microbenchmarking and Multiple Level Analysis Dissecting the NVidia Turing T4 GPU via Microbenchmarking

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-10T17:37:52.067387Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T17:37:52.067387Z digest=sha256:bd2c36c77a080798da756e8cf98d3cfceb7bde61a0c26cdee05cccca49dee9a5

Observation 22e3f90b-8e0d-4004-9cad-73c0ce5543ac · inbound

Dissecting the NVIDIA Blackwell Architecture with Microbenchmarks cites this paper.

Dissecting the NVIDIA Blackwell Architecture with Microbenchmarks Dissecting the NVidia Turing T4 GPU via Microbenchmarking

Reference 2019

Resolution
unresolved
no resolver link, observed 2026-08-06T17:31:26.853075Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:31:26.853075Z digest=sha256:f1e33b78a99b92ddf85ad291d5ff6cf6a12eecf9235548ab5f520ab838107e14

Observation ddd27145-4aa4-4b4e-bd39-8a1262afe34b · inbound

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration cites this paper.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Dissecting the NVidia Turing T4 GPU via Microbenchmarking

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-05T16:03:34.362212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T16:03:34.362212Z digest=sha256:214332385e8587a81900c588d2d1d09680bfff7195196c6652d3473ad56bf0ef

Observation de62ce88-8ba5-42b1-bfdd-7fdfe73d05e5 · inbound

Are Large Language Models Economically Viable for Industry Deployment? cites this paper.

Are Large Language Models Economically Viable for Industry Deployment? Dissecting the NVidia Turing T4 GPU via Microbenchmarking

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-05-11T13:01:19.236481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-05-10T02:28:12.686424Z digest=sha256:f01fdb4422083782708662cb78729be28d84a0f83fa431888875e293dfe36334

Observation 0f223464-4502-452f-bbac-12f3e255a74f · inbound

Non-Monotonic Latency in Apple MPS Decoding: KV Cache Interactions and Execution Regimes cites this paper.

Non-Monotonic Latency in Apple MPS Decoding: KV Cache Interactions and Execution Regimes Dissecting the NVidia Turing T4 GPU via Microbenchmarking

Reference 23

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T07:46:42.731369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-05-12T01:53:36.091037Z digest=sha256:20941c2f1fb612ce114e313fc4a3cc9fa729351e6e715783189a711360eda8cd

Observation c6768e14-ded2-4fb7-a511-2d2dd99410de · inbound

Non-Monotonic Latency in Apple MPS Decoding: KV Cache Interactions and Execution Regimes cites this paper.

Non-Monotonic Latency in Apple MPS Decoding: KV Cache Interactions and Execution Regimes Dissecting the NVidia Turing T4 GPU via Microbenchmarking

Reference 23

Resolution
metadata mismatch
local_arxiv, observed 2026-05-15T05:09:46.094543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-05-15T05:05:12.267070Z digest=sha256:35af9aa09c19f7ba58b26c341c6e50c68905dbcdaf8766e3ab8e61b2c5c4eae1

Observation 88a03382-9742-434e-88f5-5abbdf9d8184 · inbound

Rigel: Reverse-Engineering the Metal 4.1 Tensor Compute Path on the Apple M4 Max GPU cites this paper.

Rigel: Reverse-Engineering the Metal 4.1 Tensor Compute Path on the Apple M4 Max GPU Dissecting the NVidia Turing T4 GPU via Microbenchmarking

Reference 7

Resolution
verified exact
local_arxiv, observed 2026-07-03T14:08:21.716274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-06-27T07:14:52.640891Z digest=sha256:b76a797425adcd757778791d2ba00aa15499992dfa4948cb23ad83426b9038a1

Observation 19e44833-e29a-485d-af8b-97c847fa9e8d · inbound

DGNA: Dissecting GPU NUMA Architecture through Microbenchmarking and Data Analysis cites this paper.

DGNA: Dissecting GPU NUMA Architecture through Microbenchmarking and Data Analysis Dissecting the NVidia Turing T4 GPU via Microbenchmarking

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-01T11:22:36.709673Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T11:22:36.709673Z digest=sha256:612d4b5aff75ac92df865efd1ebf095cc3ff87c0bdb1bb156d66b90b30695d06

Observation 00e52511-32b0-4d21-84dc-b89cbcbc2eff · inbound

Parallel Cascaded Recursive Filtering on Multi-Core CPUs and GPUs cites this paper.

Parallel Cascaded Recursive Filtering on Multi-Core CPUs and GPUs Dissecting the NVidia Turing T4 GPU via Microbenchmarking

Reference 32

Resolution
unresolved
no resolver link, observed 2026-07-30T13:23:04.710427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-30T13:23:04.710427Z digest=sha256:e65e68b4aadc88d74af98ac1427acc66a278a25c28d5fa1b524d522b70935c8f