Paper Citation Record · LEDGER

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach

As of 23 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 1 inbound Pith citation observation for arXiv:2501.01057.

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

pith.paper-citation-record.v1
2501.01057 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:40:14.135782Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:30:40.910983Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T14:30:41.012351Z

Reference resolution

48 of 48 outbound references displayed

verified exact1
verified fuzzy46
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c271856d-5129-4181-9c1d-eaac6210a2ef · outbound

This paper cites Edge computing: Vision and challenges,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Edge computing: Vision and challenges,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.872455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6a104d2d-6214-4b54-bd4e-a8bd314d1fe4 · outbound

This paper cites Pcie vs. 5g: The importance of hpc at the edge.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Pcie vs. 5g: The importance of hpc at the edge

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.857627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation da9897d6-13b3-46d5-b8d6-6eed2322550f · outbound

This paper cites 5g enabled energy innovation: Advanced wireless networks for science,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach 5g enabled energy innovation: Advanced wireless networks for science,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.842818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation df7b45ac-0d84-49e8-a6ba-dea5f78b4f33 · outbound

This paper cites Auto-tuning full applications: A case study,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Auto-tuning full applications: A case study,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.828530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9ed56c4c-4949-421c-a879-c3d03f19c13d · outbound

This paper cites Automated reasoning and detection of specious configuration in large systems with symbolic execution,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Automated reasoning and detection of specious configuration in large systems with symbolic execution,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.814743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9ebdf36e-67a8-4de2-869b-3831e795b692 · outbound

This paper cites Software challenges in extreme scale systems,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Software challenges in extreme scale systems,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.800720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation bff6e93c-b748-4719-9e96-9b3c72cbe162 · outbound

This paper cites The antarex approach to autotuning and adaptivity for energy efficient hpc systems,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach The antarex approach to autotuning and adaptivity for energy efficient hpc systems,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.786675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e5d291f4-2483-4050-ae1f-0374ff7c768b · outbound

This paper cites Bestconfig: tapping the performance potential of systems via automatic configuration tuning,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Bestconfig: tapping the performance potential of systems via automatic configuration tuning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.772625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 122474cd-0a33-476c-8cc6-4af950ad92e1 · outbound

This paper cites d- simplexed: Adaptive delaunay triangulation for performance modeling and prediction on big data analytics,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach d- simplexed: Adaptive delaunay triangulation for performance modeling and prediction on big data analytics,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.757540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a4a51ee9-2c48-434f-be38-ceb0eaac8045 · outbound

This paper cites Optimization by simulated annealing,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Optimization by simulated annealing,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.743310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 127be446-3b90-4868-a567-93e6ea9e589f · outbound

This paper cites Particle swarm optimization,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Particle swarm optimization,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.728907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation dc867ad2-d5c9-490f-9730-bb63c6dd0485 · outbound

This paper cites {TVM}: An automated {End-to-End} optimizing compiler for deep learning,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach {TVM}: An automated {End-to-End} optimizing compiler for deep learning,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.713825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5b917c51-5a1e-433b-9479-d1b2244abfd9 · outbound

This paper cites Rfhoc: A random-forest approach to auto-tuning hadoop’s configura- tion,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Rfhoc: A random-forest approach to auto-tuning hadoop’s configura- tion,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.699172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d2a73d00-3259-460e-8d29-2eafbcd11bed · outbound

This paper cites Efficient performance prediction for apache spark,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Efficient performance prediction for apache spark,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.684013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation dbd947ee-2bc3-4819-8b87-62e7e41e924a · outbound

This paper cites Datasize-aware high dimensional configu- rations auto-tuning of in-memory cluster computing,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Datasize-aware high dimensional configu- rations auto-tuning of in-memory cluster computing,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.668916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9031c2a6-7f42-4098-88c1-fa5357a2fa0c · outbound

This paper cites Bliss: auto-tuning complex applications using a pool of diverse lightweight learning models,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Bliss: auto-tuning complex applications using a pool of diverse lightweight learning models,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.654315Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 03a2aafa-a707-4685-a12e-4b5ab5c49851 · outbound

This paper cites Autotuning in High-Performance Computing Applications,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Autotuning in High-Performance Computing Applications,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.639976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 72cd41ac-cab9-4cd4-82d0-c71d1cd69078 · outbound

This paper cites Multitask and Transfer Learning for Autotuning Exascale Applications.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Multitask and Transfer Learning for Autotuning Exascale Applications

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-10T22:40:14.179846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 059c1a68-6531-4121-a4f1-bf7a63d61528 · outbound

This paper cites Boot- strapping parameter space exploration for fast tuning,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Boot- strapping parameter space exploration for fast tuning,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.624559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation fc36f280-5f6c-4b75-87ff-57c5f19d71f9 · outbound

This paper cites Artemis: Automatic runtime tuning using machine learning,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Artemis: Automatic runtime tuning using machine learning,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.608384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a3d4101c-d725-4fb1-bf06-25a8eb186461 · outbound

This paper cites Turbo: A cost- efficient configuration-based auto-tuning approach for cluster-based big data frameworks,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Turbo: A cost- efficient configuration-based auto-tuning approach for cluster-based big data frameworks,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.593289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 72fa40e0-1a82-4374-b022-55e9c7146f33 · outbound

This paper cites Conex: Efficient exploration of big-data system configurations for better performance,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Conex: Efficient exploration of big-data system configurations for better performance,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.578621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 08455729-affb-4e0d-8337-e270fcd1daa0 · outbound

This paper cites Hdconfigor: automatically tuning high dimensional configuration parameters for log search engines,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Hdconfigor: automatically tuning high dimensional configuration parameters for log search engines,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.562754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 08e02977-144a-466c-9b42-c9d281da79d6 · outbound

This paper cites Locat: Low-overhead online configuration auto-tuning of spark sql applications,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Locat: Low-overhead online configuration auto-tuning of spark sql applications,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.547573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b69f25fe-9c13-4aa9-979c-8c0d5f6378b2 · outbound

This paper cites Introduction to multi-armed bandits,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Introduction to multi-armed bandits,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T22:40:14.027425Z

Source-reported events for the cited work

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Observation 3262ab0e-2b92-4518-8d7b-ed7199f09627 · outbound

This paper cites Pure exploration in finitely-armed and continuous-armed bandits,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Pure exploration in finitely-armed and continuous-armed bandits,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.522298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a2dba2cb-541a-401a-b1ea-25ae1d76a751 · outbound

This paper cites Non-stochastic best arm identification and hyperparameter optimization,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Non-stochastic best arm identification and hyperparameter optimization,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.508607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4ac18c44-4084-4209-8090-00462c54150d · outbound

This paper cites Simple regret for infinitely many armed bandits,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Simple regret for infinitely many armed bandits,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.494535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5d8c687e-58d7-449b-9081-298ba653a799 · outbound

This paper cites Hyperband: A novel bandit-based approach to hyperparameter opti- mization,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Hyperband: A novel bandit-based approach to hyperparameter opti- mization,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.479744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 71d85d51-41a2-4d5c-858d-cb8359f44ebd · outbound

This paper cites Portfolio choices with orthogonal bandit learning,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Portfolio choices with orthogonal bandit learning,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.465521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 79f42406-5aea-4a8d-9f79-9305d3629dea · outbound

This paper cites Input warping for bayesian optimization of non-stationary functions,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Input warping for bayesian optimization of non-stationary functions,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.450534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2bca65d5-be03-46fb-9a7d-3c14f47bdb47 · outbound

This paper cites Waggle: An open sensor platform for edge computing,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Waggle: An open sensor platform for edge computing,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.436340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ea8405ff-cc9c-4766-bc56-bd8de978f442 · outbound

This paper cites Sage: A distributed software-defined sensor network,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Sage: A distributed software-defined sensor network,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.422198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4601cd7f-00ee-4aed-b52f-308b7d98fe1a · outbound

This paper cites Optimizing cloud motion estimation on the edge with phase correlation and optical flow,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Optimizing cloud motion estimation on the edge with phase correlation and optical flow,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.407849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b01a02de-f2e3-4316-a61b-1704e773ab86 · outbound

This paper cites Goal-driven scheduling model in edge computing for smart city applications,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Goal-driven scheduling model in edge computing for smart city applications,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.393510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b07790e5-9e5f-4111-b1de-97ded1859252 · outbound

This paper cites Intersecting needs and challenges in scalable operating system research.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Intersecting needs and challenges in scalable operating system research

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.378858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c29c4c7d-ea21-4474-b337-95e2113fe740 · outbound

This paper cites Automating hpc model selection on edge devices,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Automating hpc model selection on edge devices,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.364316Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3444c971-cc54-4b25-8994-5b075f29bf86 · outbound

This paper cites Kripke-a massively parallel transport mini-app,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Kripke-a massively parallel transport mini-app,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.348623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1d5283cc-08a7-40b4-8056-f4b3a650032a · outbound

This paper cites Quantitative performance assessment of proxy apps and parents,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Quantitative performance assessment of proxy apps and parents,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.333290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c1004eb8-cc9d-416f-8838-8054b3a007f7 · outbound

This paper cites Using confidence bounds for exploitation-exploration trade- offs,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Using confidence bounds for exploitation-exploration trade- offs,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.318525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1465b60a-b92f-4da1-abd4-e6042d9cebac · outbound

This paper cites Security analysis of iot protocols: A focus in coap,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Security analysis of iot protocols: A focus in coap,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.304192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a61becec-b5b4-4eb1-a960-f756863266cd · outbound

This paper cites Characterizing the per- formance of accelerated jetson edge devices for training deep learning models,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Characterizing the per- formance of accelerated jetson edge devices for training deep learning models,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.289739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 960962b9-fba1-4214-a73e-fb345ffe4ce0 · outbound

This paper cites Clustering algo- rithms on low-power and high-performance devices for edge computing environments,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Clustering algo- rithms on low-power and high-performance devices for edge computing environments,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.274835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c3ab9b3f-654c-481e-83b9-a71fff4dbcfc · outbound

This paper cites End-to-end energy models for edge cloud-based iot platforms: Application to data stream analysis in iot,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach End-to-end energy models for edge cloud-based iot platforms: Application to data stream analysis in iot,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.257868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 85ded9d6-ebba-49ba-9620-63cfaf5c9fe5 · outbound

This paper cites Performance modeling under resource constraints using deep transfer learning,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Performance modeling under resource constraints using deep transfer learning,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.242227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation cc40ce2d-9bb0-4332-94df-6efb72f3a1d7 · outbound

This paper cites hypre: A library of high performance preconditioners,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach hypre: A library of high performance preconditioners,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.226891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 14aad446-451e-49cc-ab8b-e2a2f4c2f0b0 · outbound

This paper cites Clomp: Ac- curately characterizing openmp application overheads,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Clomp: Ac- curately characterizing openmp application overheads,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.210503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 615cf6be-f0d7-4dbc-ae81-e83d7aa3a8c1 · outbound

This paper cites Lulesh 2.0 updates and changes,.

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach Lulesh 2.0 updates and changes,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:14.195141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

Observation a68c7276-b5d8-48ab-95ba-87979147dbcb · inbound

Enhanced Intrusion Detection in IIoT Networks: A Lightweight Approach with Autoencoder-Based Feature Learning cites this paper.

Enhanced Intrusion Detection in IIoT Networks: A Lightweight Approach with Autoencoder-Based Feature Learning HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach

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