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 space–time 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.

► A hierarchical aggregation model to achieve performance visualization scalability.
► Evaluation with large scale synthetic traces of up to 100 thousands entities.
► Four KAAPI work-stealing scenarios are shown, with the identification of execution bottlenecks.
► Model is also evaluated with one MPI scenario based on the NAS-EP benchmark.
► The Triva visualization tool implements the model and is freely distributed.