Applications’ performance is influenced by the mapping of processes to computing nodes, the frequency and volume of exchanges among processing elements, the network capacity, and the routing protocol. A poor mapping of application processes degrades performance and wastes resources. As process mapping is frequently ignored as an explicit optimization step (since the system typically offers a default mapping), users may lack awareness of their applications’ communication behavior, and the opportunities for improving performance through mapping are often unclear. This work studies the impact of application process mapping on several processor topologies. We propose and apply a generic workflow that renders mapping as an explicit optimization step to a set of four applications, twelve mapping algorithms, and three direct network topologies. We assess the mappings’ quality in terms of volume, frequency, and distance of exchanges using metrics such as dilation (measured in hop·Byte). With a parallel trace-based simulator, we predict the applications’ execution on the three topologies using the twelve mappings. To ensure the correctness of the simulations, we compare the pre- and post-simulation results. This work emphasizes the importance of process mapping as an explicit optimization step and offers a solution for parallel applications to exploit the full potential of the allocated resources on a given system.