Performance is a key feature of parallel computing systems. However, the achieved performance when a certain parallel program is executed is significantly lower than the maximal theoretical performance of the parallel computing system. The model-based performance evaluation may be used to support the performance-oriented program development for parallel computing systems. In this book chapter we present a hybrid approach for performance modeling and prediction of parallel computing systems, which combines mathematical modeling and discrete-event simulation. We use mathematical modeling to develop parameterized performance models for components of the system. Thereafter, we use discrete-event simulation to describe the structure of system and the interaction among its components. As a result, we obtain a high-level performance model, which combines the evaluation speed of mathematical models with the structure awareness and fidelity of the simulation model. We evaluate empirically our approach with a real-world material science program that comprises more than 15,000 lines of code.