Pith. sign in

Paper Citation Record · LEDGER

MIOpen: An Open Source Library For Deep Learning Primitives

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1910.00078.

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

pith.paper-citation-record.v1
1910.00078 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:57:52.368856Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T11:16:09.577764Z

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 c6dea6d6-cfd9-47e5-a421-1fedca7475e1 · inbound

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO cites this paper.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO MIOpen: An Open Source Library For Deep Learning Primitives

Reference 66

Resolution
unresolved
no resolver link, observed 2026-08-07T04:57:52.368856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:57:52.368856Z digest=sha256:0df7e5ea50e6e8db27a3837101a647941a9dfc8c599158a9a166b408679bff85

Observation 8eca48f5-0bda-4d7d-8a6a-391063228a90 · inbound

Evaluating Cross-Architecture Performance Modeling of Distributed ML Workloads Using StableHLO cites this paper.

Evaluating Cross-Architecture Performance Modeling of Distributed ML Workloads Using StableHLO MIOpen: An Open Source Library For Deep Learning Primitives

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-05-11T11:16:09.581722Z

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.

source=pdf_text observed=2026-05-10T15:02:42.806073Z digest=sha256:3326644fb2195f1caf6126370ba63a98a2a709dcda1dcf1bdaf723616c2d40a3