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This is a Quantitative Study study.

October 20, 2025Open Access

Automatic Metadata Capture and Processing for High-Performance Workflows

Key Points

Collecting and reorganizing metadata significantly enhances workflow performance analysis for researchers, allowing better insights.
Experimenting with two formats for metadata storage enables uniformity, thus improving accessibility and usability of the data.
Utilizing the FAIR principles emphasizes the importance of reproducibility in research using heterogeneous computing architectures.
This software addresses the complexity in modern workflows, making performance data more manageable for researchers.

Abstract

Modern workflows run on increasingly heterogeneous computing architectures and with this heterogeneity comes additional complexity. We aim to apply the FAIR principles for research reproducibility by developing software to collect metadata annotations for workflows run on HPC systems. We experiment with two possible formats to uniformly store these metadata, and reorganize the collected metadata to be as easy to use as possible for researchers studying their workflow performance.

Automatic Metadata Capture and Processing for High-Performance Workflows

Key Points

Abstract

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