Automatic, efficient and scalable provenance registration for FAIR HPC workflows
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hdl:2117/384589
Document typeConference report
Defense date2022
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessOpen Access
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ProjectBSC - COMPUTACION DE ALTAS PRESTACIONES VIII (AEI-PID2019-107255GB-C21)
EOSC-Life - Providing an open collaborative space for digital biology in Europe (EC-H2020-824087)
EOSC-Life - Providing an open collaborative space for digital biology in Europe (EC-H2020-824087)
Abstract
Provenance registration is becoming more and more important, as we increase the size and number of experiments performed using computers. In particular, when provenance is recorded in HPC environments, it must be efficient and scalable. In this paper, we propose a provenance registration method for scientific workflows, efficient enough to run in supercomputers (thus, it could run in other environments with more relaxed restrictions, such as distributed ones). It also must be scalable in order to deal with large workflows, that are more typically used in HPC. We also target transparency for the user, shielding them from having to specify how provenance must be recorded. We implement our design using the COMPSs programming model as a Workflow Management System (WfMS) and use RO-Crate as a well-established specification to record and publish provenance. Experiments are provided, demonstrating the run time efficiency and scalability of our solution.
CitationSirvent, R. [et al.]. Automatic, efficient and scalable provenance registration for FAIR HPC workflows. A: IEEE Workshop on Workflows in Support of Large-Scale Science. "Proceedings of WORKS22: 17th Workshop on Workflows in Support of Large-Scale Science". Institute of Electrical and Electronics Engineers (IEEE), 2022, p. 1-9. ISBN 978-1-6654-5191-8. DOI 10.1109/WORKS56498.2022.00006.
ISBN978-1-6654-5191-8
Publisher versionhttps://ieeexplore.ieee.org/document/10023945
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