Making Machine Learning Datasets and Models FAIR for HPC: A Methodology and Case Study
This addresses the problem of low FAIRness in HPC machine learning datasets and models, which is incremental as it applies existing FAIR principles to a specific domain.
The paper tackles the lack of FAIR principles adoption in machine learning for HPC by designing a methodology to improve the findability, accessibility, interoperability, and reusability of datasets and models, resulting in an increase in FAIRness score from 19.1% to 83.0% in a 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%.