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Capturing Provenance for Runtime Data Analysis in Computational Science and Engineering Applications

Abstract : Capturing provenance data for runtime analysis has several challenges in high performance computational science engineering applications. The main issues are avoiding significant overhead in data capture, loading and runtime query support; and coupling provenance capture mechanisms with applications built with highly efficient numerical libraries, and visualization frameworks targeted to high performance environments. This work presents DfA-prov, an approach to capture provenance data and domain data aiming at high performance applications.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-03108922
Contributor : Isabelle Gouat <>
Submitted on : Wednesday, January 13, 2021 - 2:56:05 PM
Last modification on : Thursday, January 14, 2021 - 3:31:20 AM
Long-term archiving on: : Wednesday, April 14, 2021 - 6:48:13 PM

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Vítor Silva, Renan Souza, Jose Camata, Daniel de Oliveira, Patrick Valduriez, et al.. Capturing Provenance for Runtime Data Analysis in Computational Science and Engineering Applications. 7th International Provenance and Annotation Workshop (IPAW), Jul 2018, London, United Kingdom. pp.183-187, ⟨10.1007/978-3-319-98379-0_15⟩. ⟨lirmm-03108922⟩

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