Performance analysis is widely used to identify performance issues of parallel applications. However, complex communications and data dependence, as well as the interactions between different kinds of performance issues make high-efficiency performance analysis even harder. Although a large number of performance tools have been designed, accurately pinpointing root causes for such complex performance issues still needs specific in-depth analysis. To implement each such analysis, significant human efforts and domain knowledge are normally required.

To reduce the burden of implementing accurate performance analysis, we propose a domain specific programming framework, named PerFlow. PerFlow abstracts the step-by-step process of performance analysis as a dataflow graph. This dataflow graph consists of main performance analysis sub-tasks, called passes, which can either be provided by PerFlow’s built-in analysis library, or be implemented by developers to meet their requirements. Moreover, to achieve effective analysis, we propose a Program Abstraction Graph to represent the performance of a program execution and then leverage various graph algorithms to automate the analysis. We demonstrate the efficacy of PerFlow by three case studies of real-world applications with up to 700K lines of code. Results show that PerFlow significantly eases the implementation of customized analysis tasks. In addition, PerFlow is able to perform analysis and locate performance bugs automatically and effectively.