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• W4387005232 abstract "Scientific computing communities increasingly run their experiments using complex data- and compute-intensive workflows that utilize distributed and heterogeneous architectures targeting numerical simulations and machine learning, often executed on the Department of Energy Leadership Computing Facilities (LCFs). We argue that a principled, systematic approach to implementing FAIR principles at scale, including fine-grained metadata extraction and organization, can help with the numerous challenges to performance reproducibility posed by such workflows. We extract workflow patterns, propose a set of tools to manage the entire life cycle of performance metadata, and aggregate them in an HPC-ready framework for reproducibility (RECUP). We describe the challenges in making these tools interoperable, preliminary work, and lessons learned from this experiment." @default.
• W4387005232 created "2023-09-26" @default.
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• W4387005232 date "2023-10-09" @default.
• W4387005232 modified "2023-09-26" @default.
• W4387005232 title "Building the I (Interoperability) of FAIR for Performance Reproducibility of Large-Scale Composable Workflows in RECUP" @default.
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• W4387005232 doi "https://doi.org/10.1109/e-science58273.2023.10254808" @default.
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