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

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.