ProvDeploy: Provenance-oriented Containerization of High Performance Computing Scientific Workflows
CoRR(2024)
摘要
Many existing scientific workflows require High Performance Computing
environments to produce results in a timely manner. These workflows have
several software library components and use different environments, making the
deployment and execution of the software stack not trivial. This complexity
increases if the user needs to add provenance data capture services to the
workflow. This manuscript introduces ProvDeploy to assist the user in
configuring containers for scientific workflows with integrated provenance data
capture. ProvDeploy was evaluated with a Scientific Machine Learning workflow,
exploring containerization strategies focused on provenance in two distinct HPC
environments
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