Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems

12:00 - 12:15
PerfAnalyzer: Revealing Performance Trends using Version Oriented Visual Analysis of Scientific Software

Sayef Azad Sakin
University of Utah, USA
Los Alamos National Laboratory, USA

James Ahrens
Los Alamos National Laboratory, USA

Understanding the behavior of scientific software is essential in maintaining the integrity and transparency of computational research. Tracking the changes in computational parameters (input-output parameters, configuration parameters for hardware and software) across different versions of software executions helps in understanding by providing a context to correlate the outcomes with meaningful changes and to interpret the results reliably. Diverse computing environments and software platforms add complexity in tracking the evolution of computational parameters across multiple runs. We present PerfAnalyzer, an interactive dashboard to simplify the collection, management, and visual analysis of computational parameters across Git commits. We demonstrate the usefulness of the dashboard in identifying performance issues through a case study on collecting and analyzing computational parameters of the CloverLeaf mini-application. The results of the case study show PerfAnalyzer’s ability to highlight performance changes across versions and identify parameters related to the changes that are difficult to locate using isolated measurements.