Parallel performance wizard - framework and techniques for parallel application optimization

Developing a high-performance parallel application is difficult. Given the complexity of high-performance parallel programs, developers often must rely on performance analysis tools to help them improve the performance of their applications. While many tools support analysis of message-passing programs, tool support is limited for applications written in other programming models such as those in the partitioned global-address-space (PGAS) family, which is of growing importance. Existing tools that support message-passing models are difficult to extend to support other parallel models because of the differences between the paradigms. In this dissertation, we present work on the Parallel Performance Wizard (PPW) system, the first general-purpose performance system for parallel application optimization. The complete research is divided into three parts. First, we introduce a model-independent PPW performance tool framework for parallel application analysis. Next, we present a new scalable, model-independent PPW analysis system designed to automatically detect, diagnose, and possibly resolve bottlenecks within a parallel application. Finally, we discuss case studies to evaluate the effectiveness of PPW and conclude with contributions and future directions for the PPW project.