Pilot corpus: NIH-funded biomedical datasets. Any DOI can be scored on demand — network effects sharpen as coverage grows.

Training of deep learning pipelines on memory-constrained GPUs via segmented fused-tiled execution

PubMed(2022)10.1145/3497776.3517766Source: DataRank Database

Training of deep learning pipelines on memory-constrained GPUs via segmented fused-tiled execution is a research paper published in PubMed (2022). On theSindex it has a DataRank of 0. It has been cited 1 time.

N/A

0DataRank · unranked

Scored on demand from live citation data

Open Access1 citations

Free full text

Cite:

DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?

›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (6 grants)Impact (FWCI 0.09)OA: goldTopics (27)IDs (PubMed, OpenAlex)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

FAIR checklist signals are shown for context only and do not affect DataRank scoring.

Run a calibrated FAIR evaluation for this paper →

We only score data papers we can read in full — never from an abstract alone.

Authors (6)

Saurabh Raje ORCID,Atanas Rountev ORCID,Gerald Sabin ORCID,Aravind Sukumaran-Rajam ORCID,P. Sadayappan ORCID

Live enrichment

PubMed:35876769 OpenAlex:https://openalex.org/W4221003081

NSF (National Science Foundation)

Grant: 2018016, 2119677, 2118737

NIH (National Institutes of Health)

Grant: R41EB032722

National Science Foundation

Grant: 2118737

Collaborative Research: PPoSS: Planning: Model-Driven Compiler Optimization and Algorithm-Architecture Co-Design for Scalable Machine Learning

National Science Foundation

Grant: 2018016

SHF: Small: Tools for Productive High-performance Computing with GPUs

National Science Foundation

Grant: 2119677

Collaborative Research: PPoSS: Planning: Model-Driven Compiler Optimization and Algorithm-Architecture Co-Design for Scalable Machine Learning

National Institutes of Health

Grant: 1R41EB032722-01

Enabling Next Generation Machine Learning for Large Scale Image Analysis

FWCI

0.09

Citation Percentile

0.3%

Citation Trend

2024

goldLicense: cc-by

https://dl.acm.org/doi/pdf/10.1145/3497776.3517766

publisherhttps://dl.acm.org/doi/10.1145/3497776.3517766

https://doi.org/10.1145/3497776.3517766

repositoryhttps://pubmed.ncbi.nlm.nih.gov/35876769

Fields of Study

Advanced Neural Network ApplicationsStochastic Gradient Optimization TechniquesDomain Adaptation and Few-Shot Learning0202 electrical engineering, electronic engineering, information engineering02 engineering and technology

Keywords

Computer sciencePipeline (software)Deep learningPipeline transportArtificial intelligenceGraphicsInferenceProcess (computing)Parallel computingGeneral-purpose computing on graphics processing unitsGraphics processing unitGraphics pipelineArtificial neural networkCUDAComputer engineeringComputer architectureComputer graphicsMachine learningComputer graphics (images)3D computer graphicsProgramming languageArticle