PerfExplorer is a framework for parallel performance data mining and knowledge discovery. The framework architecture enables the development and integration of data mining operations that will be applied to large-scale parallel performance profiles.
The overall goal of the PerfExplorer project is to create a software to integrate sophisticated data mining techniques in the analysis of large-scale parallel performance data.
PerfExplorer supports clustering, summarization, association, regression, and correlation. Cluster analysis is the process of organizing data points into logically similar groupings, called clusters. Summarization is the process of describing the similarities within, and dissimilarities between, the discovered clusters. Association is the process of finding relationships in the data. One such method of association is regression analysis, the process of finding independent and dependent correlated variables in the data. In addition, comparative analysis extends these operations to compare results from different experiments, for instance, as part of a parametric study.
In addition to the data mining operations available, the user may optionally choose to perform comparative analysis. The types of charts available include time-steps per second, relative efficiency and speedup of the entire application, relative efficiency and speedup of one event, relative efficiency and speedup for all events, relative efficiency and speedup for all phases and runtime breakdown of the application by event or by phase. In addition, when the events are grouped together, such as in the case of communication routines, yet another chart shows the percentage of total runtime spent in that group of events. These analyses can be conducted across different combinations of parallel profiles and across phases within an execution.