Introduces a method and a tool for optimizing programs on massively parallel computing systems. The tool is scalable with respect to its implementation and in the way it presents performance data. A major feature contributing to the scalable representation of performance data is the ability to focus measurements on points of interest in the program execution by specifying behavioral attributes. Behavioral attributes are given as thresholds to the results of other measurements. Thus, a direct link between the results of different measurements can be made, which enables the user to link global system behavior to the execution of individual program parts. With regard to the implementation, it is shown how the distributed measurement evaluation and the online mode of operation enable the tool to handle the problems of massively parallel computing platforms.