Scalable, automated performance analysis with tau and perfexplorer

Scalable performance analysis is a challenge for parallel development tools. The potential size of data sets and the need to compare results from multiple experiments presents a challenge to manage and process the information, and to characterize the performance of parallel appli-cations running on potentially hundreds of thousands of processor cores. In addition, many exploratory analysis processes represent potentially repeatable processes which can and should be automated. In this paper, we will discuss the current version of PerfExplorer, a performance analysis framework which provides dimension reduction, clustering and correlation analysis of individual trails of large dimensions, and can perform relative performance analysis between multiple application executions. PerfExplorer analysis processes can be captured in the form of Python scripts, automating what would otherwise be time-consuming tasks. We will give examples of large-scale analysis results, and discuss the future development of the framework, including the encoding and processing of expert performance rules, and the increasing use of performance metadata.