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Hybrid MPI+threads programming is gaining prominence as an alternative to the traditional "MPI everywhere'" model to better handle the disproportionate increase in the number of cores compared with other on-node resources. Current implementations of these two models represent the two extreme cases of communication resource sharing in modern MPI implementations. In the MPI-everywhere model, each MPI process has a dedicated set of communication resources (also known as endpoints), which is ideal for performance but is resource wasteful. With MPI+threads, current MPI implementations share a single communication endpoint for all threads, which is ideal for resource usage but is hurtful for performance. In this paper, we explore the tradeoff space between performance and communication resource usage in MPI+threads environments. We first demonstrate the two extreme cases---one where all threads share a single communication endpoint and another where each thread gets its own dedicated communication endpoint (similar to the MPI-everywhere model) and showcase the inefficiencies in both these cases. Next, we perform a thorough analysis of the different levels of resource sharing in the context of Mellanox InfiniBand. Using the lessons learned from this analysis, we design an improved resource-sharing model to produce \emph{scalable communication endpoints} that can achieve the same performance as with dedicated communication resources per thread but using just a third of the resources.