Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T12:47:05.977447Z
Paper Citation Record · LEDGER
Understanding GEMM Performance and Energy on NVIDIA Ada Lovelace: A Machine Learning-Based Analytical Approach
As of 18 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 1 inbound Pith citation observation for arXiv:2411.16954.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-05T16:03:37.761446Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-05T16:03:38.623641Z
Reference resolution
13 of 13 outbound references displayed
- verified exact0
- verified fuzzy13
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation d294ce28-c6fc-4e01-a098-7e8b9c503f5a · outbound
This paper cites Performance-aware energy-efficient GPU frequency selection using DNN-based models,.
Understanding GEMM Performance and Energy on NVIDIA Ada Lovelace: A Machine Learning-Based Analytical Approach Performance-aware energy-efficient GPU frequency selection using DNN-based models,
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T12:47:06.148592Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation faed6d70-3e21-4eea-b6d2-969fb25bc3bb · outbound
This paper cites DSO: A GPU energy efficiency optimizer by fusing dynamic and static information,.
Understanding GEMM Performance and Energy on NVIDIA Ada Lovelace: A Machine Learning-Based Analytical Approach DSO: A GPU energy efficiency optimizer by fusing dynamic and static information,
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T12:47:06.136090Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation ced2f81d-4b1b-4657-a20c-847b3748b034 · outbound
This paper cites Energy-aware high- performance computing: Survey of state-of-the-art tools, techniques, and environments,.
Understanding GEMM Performance and Energy on NVIDIA Ada Lovelace: A Machine Learning-Based Analytical Approach Energy-aware high- performance computing: Survey of state-of-the-art tools, techniques, and environments,
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T12:47:06.125601Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 22f6fa2c-8898-4ad5-a795-450e7b5f0f27 · outbound
This paper cites Energy-aware GPU performance prediction and optimization framework,.
Understanding GEMM Performance and Energy on NVIDIA Ada Lovelace: A Machine Learning-Based Analytical Approach Energy-aware GPU performance prediction and optimization framework,
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T12:47:06.114382Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 7a00080e-d33d-4c54-9b20-5011036ebbe9 · outbound
This paper cites CUDA C++ programming guide,.
Understanding GEMM Performance and Energy on NVIDIA Ada Lovelace: A Machine Learning-Based Analytical Approach CUDA C++ programming guide,
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T12:47:06.104033Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation c8ab6dd7-bd24-4bbd-ba02-6dc9ee50b48d · outbound
This paper cites Performance prediction of GPU-based deep learning applications,.
Understanding GEMM Performance and Energy on NVIDIA Ada Lovelace: A Machine Learning-Based Analytical Approach Performance prediction of GPU-based deep learning applications,
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T12:47:06.093681Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation b1d48b5f-1536-4af7-a897-20dc0454a460 · outbound
This paper cites A measurement study of GPU DVFS on energy conservation,.
Understanding GEMM Performance and Energy on NVIDIA Ada Lovelace: A Machine Learning-Based Analytical Approach A measurement study of GPU DVFS on energy conservation,
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T12:47:06.083557Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 2669955d-6e55-444c-8d15-3657419fdcbc · outbound
This paper cites DVFS-aware application classification to improve GPGPUs energy efficiency,.
Understanding GEMM Performance and Energy on NVIDIA Ada Lovelace: A Machine Learning-Based Analytical Approach DVFS-aware application classification to improve GPGPUs energy efficiency,
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T12:47:06.072056Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 4765b143-5bbf-43c7-8ec1-5cfe804d5f4d · outbound
This paper cites An analytical model for a GPU architecture with memory-level and thread-level parallelism awareness,.
Understanding GEMM Performance and Energy on NVIDIA Ada Lovelace: A Machine Learning-Based Analytical Approach An analytical model for a GPU architecture with memory-level and thread-level parallelism awareness,
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T12:47:06.061412Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 831c323b-02e9-4962-ab82-31c57e31a14f · outbound
This paper cites A simple model for portable and fast prediction of execution time and power consumption of GPU kernels,.
Understanding GEMM Performance and Energy on NVIDIA Ada Lovelace: A Machine Learning-Based Analytical Approach A simple model for portable and fast prediction of execution time and power consumption of GPU kernels,
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T12:47:06.050607Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation f113f0e6-dcf0-493f-be43-2400e43bb747 · outbound
This paper cites Sta- tistical power modeling of GPU kernels using performance counters,.
Understanding GEMM Performance and Energy on NVIDIA Ada Lovelace: A Machine Learning-Based Analytical Approach Sta- tistical power modeling of GPU kernels using performance counters,
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T12:47:06.039430Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 469aa780-7f02-4278-bf76-5ee50ef23067 · outbound
This paper cites Predicting the energy consumption of CUDA kernels using SimGrid,.
Understanding GEMM Performance and Energy on NVIDIA Ada Lovelace: A Machine Learning-Based Analytical Approach Predicting the energy consumption of CUDA kernels using SimGrid,
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T12:47:06.025430Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation f3ec8fc7-8598-4189-b123-37c9a73b49ad · outbound
This paper cites A preliminary empirical study of the power efficiency of matrix multiplication,.
Understanding GEMM Performance and Energy on NVIDIA Ada Lovelace: A Machine Learning-Based Analytical Approach A preliminary empirical study of the power efficiency of matrix multiplication,
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T12:47:06.010589Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Pith citing papers
Observation 8b8e1fab-3214-4f98-ab05-99f3338c3ecd · 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 Understanding GEMM Performance and Energy on NVIDIA Ada Lovelace: A Machine Learning-Based Analytical Approach
Reference 64
- Resolution
- verified exact
- local_arxiv, observed 2026-08-05T16:03:38.709223Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.