The HPE Slingshot interconnect is used on numerous supercomputers, including the top two supercomputers on the TOP500. Recently, HPE open-sourced the software stack for Slingshot introducing new opportunities for exploring alternative MPI implementations on HPE’s Cray supercomputers. This work investigates the performance implications of using Open MPI, as opposed to the traditionally bundled Cray MPICH, on systems equipped with Slingshot-11 interconnects. We focus our analysis on Adaptive Mesh Refinement (AMR) applications in this work, as they exhibit a wide variety of communication patterns, including dynamically changing communicating peers. Based on profiling and analysis of these AMR applications, we designed a targeted micro-benchmark to capture key communication patterns in AMR that can benefit from Open MPI on Slingshot-11 systems. We demonstrate that Open MPI can improve the overall execution time of AMR-based scientific applications by up to 11%. Deeper analysis using our communication pattern benchmark reveals one aspect of this performance difference. Open MPI has a much lower latency than Cray MPICH in bursty halo exchanges among even a moderately small number of processes.