A hierarchical aggregation model to achieve visualization scalability in the analysis of parallel applications ?
The analysis of large-scale parallel applications today has several issues, such as the observation and identification of unusual behavior of processes, expected state of the application, and so on. Performance visualization tools offer a wide spectrum of techniques to visually analyze the monitoring data collected from these applications. The problem is that most of the techniques were not conceived to deal with a high number of processes, in large-scale scenarios. A common example for that is the spacetime view, largely used in the performance visualization area, but limited on how much data can be analyzed at the same time. The work presented in this article addresses the problem of visualization scalability in the analysis of parallel applications, through a combination of a temporal integration technique, an aggregation model and treemap representations. Results show that our approach can be used to analyze applications composed of several thousands of processes in large-scale and dynamic scenarios.
treemaps grid computing trace visualization large-scale application analysis aggregation and reduction algorithms