The evolution of parallel computing architectures presents new challenges for developing efficient parallelized codes. The emergence of heterogeneous systems has given rise to multiple programming models, each requiring careful adaptation to maximize performance. In this context, we propose reevaluating memory layout designs for computational tasks within larger nodes by comparing various architectures. To gain insight into the performance discrepancies between shared memory and shared-address space settings, we systematically measure the bandwidth between cores and sockets using different methodologies. Our findings reveal significant differences in performance, suggesting that MPI running inside UNIX processes may not fully utilize its intranode bandwidth potential. In light of our work in the MPC thread-based MPI runtime, which can leverage shared memory to achieve higher performance due to its optimized layout, we advocate for enabling the use of shared memory within the MPI standard.