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Dynamic performance stubs provide a framework for the simulation of the performance behavior of software modules and functions. Hence, they can be used as an extension to software performance engineering methodologies. The methodology of dynamic performance stubs can be used for a gain oriented performance improvement. It is also possible to identify “hidden” bottlenecks and to prioritize optimization possibilities. Nowadays, the processing power of CPUs is mainly increasing by adding more cores to the architecture. To have benefits from this, new software is mostly designed for parallel processing, especially, in large software projects. As software performance optimizations can be difficult in these environments, new methodologies have to be defined. This paper extends and improves the methodology of CPU stubs and applies it to multi-core environments and parallel processing. The method is evaluated by means of a proof of concept. We were able to show that CPU stubs can be used to identify performance bottlenecks in parallel processing environments and to quantify the gain of different performance improvements. Hence, a new methodology for gain oriented optimization of CPU bound parallel processes has been validated in a case study.