Implementing High-Performance Geometric Multigrid Solver with Naturally Grained Messages

Structured grid linear solvers often require manually 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 PGAS 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.