1 citations · 1 across the 3 of their papers we have counts for
7 papers
Workflow Cards: Structured Summaries of Workflow Executions Using Provenance Data
Nicola Giuseppe Marchioro, Gabriele Padovani, Amal Gueroudji +5
Model Cards and Data Cards have demonstrated the value of structured, human-readable documentation for machine learning artifacts, capturing their context, parameters, limitations,…
SetGo: Metadata Readiness for Scientific AI Datasets
Sean R. Wilkinson, Polina Shpilker, Wesley Brewer
Scientific datasets intended for AI use require both computational readiness for model training and metadata readiness for discovery, sharing, and reuse. The Readiness Engine for D…
Automated Data Readiness for Scientific AI
Sean R. Wilkinson, Valentine G. Anantharaj, Jong Youl Choi +8
Leadership computing facilities steward large-scale scientific datasets that routinely require substantial transformation before serving as AI training data. However, no existing f…
Intelligent Sampling of Extreme-Scale Turbulence Datasets for Accurate and Efficient Spatiotemporal Model Training
Wesley Brewer, Murali Meena Gopalakrishnan, Matthias Maiterth +12
With the end of Moore's law and Dennard scaling, efficient training increasingly requires rethinking data volume. Can we train better models with significantly less data via intell…
Trace Replay Simulation of MIT SuperCloud for Studying Optimal Sustainability Policies
Wesley Brewer, Matthias Maiterth, Damien Fay
The rapid growth of AI supercomputing is creating unprecedented power demands, with next-generation GPU datacenters requiring hundreds of megawatts and producing fast, large swings…
HPC Digital Twins for Evaluating Scheduling Policies, Incentive Structures and their Impact on Power and Cooling
Matthias Maiterth, Wesley H. Brewer, Jaya S. Kuruvella +8
Schedulers are critical for optimal resource utilization in high-performance computing. Traditional methods to evaluate schedulers are limited to post-deployment analysis, or simul…