As single-core performance of processors is not improving significantly anymore, the computer industry is moving towards increasing the amount of cores per processor or, in the case of large-scale computers, by installing more processors per computer. Applications now need to scale in accordance with the increase of parallel computing power and software developers need to take advantage of this movement. And parallel sorting algorithms present basic building blocks for many complex applications. In this thesis, we will validate the expected execution time complexities of five state-of-the-art parallel sorting algorithms, implemented in C using MPI for parallelization, by using a scalability validation framework based on Score-P and Extra-P. For each of the parallel sorting algorithms, we will create a performance model. These models will allow us to compare their scalability behaviour to the expectations. Furthermore, we will attempt to parallelize the local sorting step of the splitter-based parallel sorting algorithms via C++11 threads, OpenMP tasks, and CUDA acceleration. We construct the performance models, on which we base our evaluations, using uniformly randomly generated data. For most of the parallel sorting algorithms, we show that the given expectations match the created models. We will discuss any other discrepancies in detail.