Automatic Performance Diagnosis of Parallel Computations with Compositional Models

Performance tuning involves a diagnostic process to locate and explain sources of program inefficiency. A performance diagnosis system can leverage knowledge of performance causes and symptoms that come from expertise with parallel computational models. This paper extends our model-based performance diagnosis approach to programs with multiple models. We study two types of model compositions (nesting and restructuring) and demonstrate how the Hercule performance diagnosis framework can automatically discover and interpret performance problems due to model nesting in the FLASH application.