Towards a framework for automated performance tuning

As part of the DARPA sponsored high productivity computing systems (HPCS) program, IBM is building petaflop supercomputers that will be fast, power-efficient, and easy to program. In addition to high performance, high productivity to the end user is another prominent goal. The challenge is to develop technologies that bridge the productivity gap - the gap between the hardware complexity and the software limitations. In addition to language, compiler, and runtime research, powerful and user-friendly performance tools are critical in debugging performance problems and tuning for maximum performance. Traditional tools have either focused on specific performance aspects (e.g., communication problems) or provided limited diagnostic capabilities, and using them alone usually do not pinpoint accurately performance problems. Even fewer tools attempt to provide solutions for problems detected. In our study, we develop an open framework that unifies tools, compiler analysis, and expert knowledge to automatically analyze and tune the performance of an application. Preliminary results demonstrated the efficiency of our approach.