Skip to Main content Skip to Navigation

Análise de dados científicos sobre múltiplas fontes de dados ao longo da execução de simulações computacionais

Vitor Silva 1, 2
2 ZENITH - Scientific Data Management
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : Large-scale computational simulations are characterized by the chaining of programs that execute increasingly complex computational models. Much of the data produced by these programs need to be analyzed by scientific domain users to validate their scientific hypotheses. However, it is not trivial since other programs must be developed to access and to capture these scientific data. In many cases, users also need to relate data produced by different simulation programs. This thesis proposes an approach that monitors, debugs, and analyzes the data element flow produced by different simulation programs. We also propose a component-based architecture, named as ARMFUL, to extract and relate scientific data generated in these several simulation steps considering a dataflow abstraction and techniques for scientific data capture. ARMFUL’s components can be instantiated on a scientific workflow system (e.g., A-Chiron) or a library of components (e.g., DfAnalyzer). We evaluate these instances using simulations in high performance computing environments. In our experimental results, our approach introduced a negligible overhead of the simulation execution time, and we perform complex queries to the scientific data.
Document type :
Theses
Complete list of metadatas

https://hal-lirmm.ccsd.cnrs.fr/tel-01830211
Contributor : Patrick Valduriez <>
Submitted on : Wednesday, July 4, 2018 - 4:51:03 PM
Last modification on : Thursday, December 13, 2018 - 4:59:44 PM
Document(s) archivé(s) le : Monday, October 1, 2018 - 1:09:06 PM

File

thesis-vitor-silva.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : tel-01830211, version 1

Collections

Citation

Vitor Silva. Análise de dados científicos sobre múltiplas fontes de dados ao longo da execução de simulações computacionais. Databases [cs.DB]. Universidade Federal de Rio de Janeiro, 2018. Portuguese. ⟨tel-01830211⟩

Share

Metrics

Record views

186

Files downloads

247