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Automatic, Efficient and Scalable Provenance Registration for FAIR HPC Workflows

Authors: Sirvent Pardell, Raül; Conejero Bañón, Francisco Javier; Lordan Gomis, Francesc; Ejarque Artigas, Jorge; Rodríguez Navas, Laura; Fernández González, José María; Capella Gutiérrez, Salvador; +1 Authors

Automatic, Efficient and Scalable Provenance Registration for FAIR HPC Workflows

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.

Country
Spain
Keywords

Supercomputers, Reproducibility, Workflow -- Management, COMPSs, RO-Crate, Supercomputadors, Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors, Provenance, Scientific workflows, Replicability, High performance computing, Cicle de treball -- Gestió, FAIR

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download
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
OpenAIRE UsageCountsDownloads provided by UsageCounts
3
Top 10%
Average
Average
202
234
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