Архив статей журнала
The paper covers an intelligent support system that allows to describe and construct solutions to various scientific problems. In this study, in particular, we consider geophysical problems. This system is being developed at the Institute of Computational Mathematics and Mathematical Geophysics of the Russian Academy of Sciences (ICMMG SB RAS) and Institute of Informatics System of the Russian Academy of Sciences (IIS SB RAS). The system contains a knowledge base, the core of which is a set of several interconnected ontologies such as the ontology of supercomputer architectures, the ontology of algorithms and methods. Ontology can be viewed as a set of concepts and how those concepts are linked. As the result, the authors present an ontological description of two geophysical problems via the means of the intelligent support system: 1) the seismic wavefield simulation and 2) the reconstruction of a seismic image through pre-stack time or depth migration. For a better visual understanding of the system described and the results obtained, the paper also contains several schematic diagrams and images.
HPC systems are complex in architecture and contain millions of components. To ensure reliable operation and efficient output, functioning of most subsystems should be supervised. This is done on the basis of collected data from various logging and monitoring systems. This means that different data sources are used, and accordingly, data analysis can face multiple issues processing this data. Some of the data subsets can be incorrect due to the malfunctioning of used sensors, monitoring system data aggregation errors, etc. This is why it is crucial to preprocess such monitoring data before analyzing it, taking into the consideration the analysis goals. The aim of this paper is, being based on the MSU HPC Center monitoring data, to propose an approach to data preprocessing of HPC monitoring systems, giving some real life examples of issues that may be faced, and recommendations for further analysis of similar datasets.