Spatial Dataflow Architectures are an emerging hardware pattern in high-performance computing, whose mesh-connected fixed-memory processing elements are tailored for structured grid kernels with two-dimensional neighborhoods. However, practical multiphysics codes are often computed on unstructured grids, which induce indirect memory accesses and high-dimensional communication patterns, making them infeasible to directly map onto said architectures. This work takes a principled, model-centric approach to partitioning unstructured problems onto spatial dataflow architectures. Through communication and memory modeling, we propose a joint decomposition that considers both the size of the application's fields and its subroutines. In particular, we automate the analysis process of the original code, define a high-dimensional decomposition that minimizes communication via space-filling curves, and apply memory optimization techniques, crucial in this memory-limited environment. We demonstrate mapping the Livermore Unstructured Lagrangian Explicit Shock Hydrodynamics (LULESH) application to the Cerebras Wafer-Scale Engine, showing that larger, unstructured grid codes can still outperform GPUs.

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