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Publicado em: 24/10/2009

Defesa de Tese em Processamento Paralelo e Distribuído dia 26/10 de Lucas Mello Schnorr

Defesa de Tese de Doutorado em Co-tutela em Processamento Paralelo e Distribuído dia 26/10 de Lucas Mello Schnorr

UNIVERSIDADE FEDERAL DO RIO GRANDE DO SUL
INSTITUTO DE INFORMÁTICA
PROGRAMA DE PÓS-GRADUAÇÃO EM COMPUTAÇÃO

DEFESA DE TESE DE DOUTORADO EM CO-TUTELA

Aluno: Lucas Mello Schnorr
Orientador: Prof. Dr. Philippe Olivier Alexandre Navaux
Orientador (INPG/Grenoble): Prof. Dr. Denis Trystram
Co-Orientador (UJF/Grenoble): Prof. Dr. Guillaume Huard

Título: Some Visualization Models applied to the Analysis of Parallel Applications
Linha de Pesquisa: Processamento Paralelo e Distribuído

Data: 26/10/2009
Hora: 10h
Local: Auditório Prof. José Mauro Volkmer de Castilho – Instituto de Informática/UFRGS

Banca Examinadora:
Prof. Dr. Jean-François Méhaut (UJF/Grenoble Université)
Prof. Dr. Siang Song (USP)
Prof. Dr. Nicolas Maillard (UFRGS)

Presidente da Banca: Prof. Dr. Philippe Olivier Alexandre Navaux

RESUMO:
Highly distributed systems such as Grids are used today to the execution of large-scale parallel applications. Some characteristics of these systems are the complex resource interconnection that might be present and the scalability. The interconnection complexity comes from the different number of hops to provide communication among applications processes and differences in network latencies and bandwidth. The scalability means that the resources can be added indefinitely just by  connecting them to the existing infrastructure. These characteristics influence directly the way parallel applications performance must be analyzed. Current traditional visualization schemes to this analysis are usually based on gantt charts with one dimension to list the monitored entities and the other dimension dedicated to time. These visualizations are generally not suited to parallel applications executed in grids. The first reason is that they were not built to offer to the developer an analysis that also shows the network topology of the resources. The second reason is that traditional visualization techniques do not scale well when thousands of monitored entities must be analyzed together.

This thesis tries to overcome the issues encountered on traditional visualization techniques for parallel applications. The main idea behind our efforts is to explore techniques from the information visualization research area and to apply them in the context of parallel applications analysis. Based on this main idea, the thesis proposes two visualization models: the three-dimensional and the visual aggregation model. The former might be used to analyze parallel applications taking into account the network topology of the resources. The visualization itself is composed of three dimensions, where two of them are used to render the topology and the third is used to represent time. The later model can be used to analyze parallel applications composed of several thousands of processes. It uses hierarchical organization of monitoring data and an information visualization technique called Treemap to  represent that hierarchy. Both models represent a novel way to visualize the behavior of parallel applications, since they are conceived considering large-scale and complex distributed systems, such as grids.

The implications of this thesis are directly related to the analysis and understanding of parallel applications executed in distributed systems. It enhances the comprehension of patterns in communication among processes and improves the possibility of matching this patterns with real network topology of grids. Although we extensively use the network topology example, the approach could be adapted with almost no changes to the interconnection provided by a middleware of a logical interconnection. With the scalable visualization technique, developers are able to look for patterns and observe the behavior of large-scale applications.

Palavras-chave: Parallel Applications, Performance Analysis, Visualization, 3D Visualization, Treemap, Scalability, Grid.