Making Machine Learning Datasets and Models FAIR for HPC: A Methodology and Case Study
The FAIR Guiding Principles aim to improve the findability, accessibility,
interoperability, and reusability of digital content by making them both human
and machine actionable. However, these principles have not yet been broadly
adopted in the domain of machine learning-based program analyses and
optimizations for High-Performance Computing (HPC). In this paper, we design a
methodology to make HPC datasets and machine learning models FAIR after
investigating existing FAIRness assessment and improvement techniques. Our
methodology includes a comprehensive, quantitative assessment for elected data,
followed by concrete, actionable suggestions to improve FAIRness with respect
to common issues related to persistent identifiers, rich metadata descriptions,
license and provenance information. Moreover, we select a representative
training dataset to evaluate our methodology. The experiment shows the
methodology can effectively improve the dataset and model's FAIRness from an
initial score of 19.1% to the final score of 83.0%.