The need for high-performance computing together with the increasing trend from single processor to parallel computer architectures has leveraged the adoption of parallel computing. To benefit from parallel computing power, usually parallel algorithms are defined that can be mapped and executed on parallel computing platforms. In general, different alternative mappings can be defined each with different performance. For small computing platforms with a limited number of processing nodes, the mapping process can be carried out manually. However, for large-scale parallel computing platforms in which hundreds of thousands of processing nodes are applied, the number of possible mapping alternatives increases dramatically, and the mapping process becomes intractable for the human engineer. To assist the parallel computing engineer, we provide a systematic approach to derive feasible mapping alternatives of parallel algorithms to parallel computing platforms. The approach includes activities for modeling the parallel algorithm and parallel computing platform, generation of feasible mapping alternatives, generation of the deployment code, and finally the deployment of the generated code to the nodes. We evaluate our approach for deriving feasible mapping alternatives for four well-known parallel algorithms. The evaluation is based on both simulations and real executions of the generated mapping alternatives.