Structured-grid linear solvers often require manual packing and unpacking of communication data to achieve high performance. Orchestrating this process efficiently is challenging, labor-intensive, and potentially error-prone. In this paper, we explore an alternative approach that communicates the data with naturally grained message sizes without manual packing and unpacking. This approach is the distributed analogue of shared-memory programming, taking advantage of the global address space in

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... AS languages to provide substantial programming ease. However, its performance may suffer from the large number of small messages. We investigate the runtime support required in the UPC ++ library for this naturally grained version to close the performance gap between the two approaches and attain comparable performance at scale using the High-Performance Geometric Multgrid (HPGMG-FV) benchmark as a driver.