Current HPC architectures are deeply hierarchical (racks, nodes, sockets, NUMA domains, caches, ...), and the mapping of MPI processes to cores can significantly influence application performance. To study hierarchy effects on MPI application performance, we propose a procedure for expressing mappings by enumerating cores in the hierarchy in different orders. We explore two use cases: MPI rank reordering for applications using subcommunicators, and core selection for applications not using all cores on a node. Results of micro-benchmarks executing collective operations in subcommunicators show a performance difference up to a factor 4 between the best and the worst rank orderings. By changing the rank orders, we observe a performance impact for the Splatt application. The evaluation of the strong scalability of a conjugate gradient benchmark shows that considering all hierarchy levels in the core selection policy can give better performance than using only options available with common MPI application launchers.