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Fast and accurate climate simulations and weather predictions are criticalfor understanding and preparing for the impact of climate change. Real-worldweather and climate modeling consist of complex compound stencil kernels thatdo not perform well on conventional architectures. Horizontal diffusion is onesuch important compound stencil found in many climate and weather predictionmodels. Recent works propose using FPGAs as an alternative to traditional CPUand GPU-based systems to accelerate compound stencil kernels. However, weobserve that compound stencil computations cannot leverage the bit-levelflexibility available on an FPGA because of its complex memory access patterns,leading to high hardware resource utilization and low peak performance. Weintroduce SPARTA, a novel spatial accelerator for horizontal diffusion weatherstencil computation. We exploit the two-dimensional spatial architecture toefficiently accelerate horizontal diffusion stencil by designing the firstscaled-out spatial accelerator using MLIR (Multi-Level IntermediateRepresentation) compiler framework. We evaluate its performance on a realcutting-edge AMD-Xilinx Versal AI Engine spatial architecture. Our real-systemevaluation results demonstrate that SPARTA outperforms the state-of-the-artCPU, GPU, and FPGA implementations by 17.1x, 1.2x, and 2.1x, respectively. Ourresults reveal that balancing workload across the available processingresources is crucial in achieving high performance on spatial architectures. Wealso implement and evaluate five elementary stencils that are commonly used asbenchmarks for stencil computation research. We freely open-source all ourimplementations to aid future research in stencil computation and spatialcomputing systems at https://github.com/CMU-SAFARI/SPARTA.

SPARTA: Spatial Acceleration for Efficient and Scalable Horizontal Diffusion Weather Stencil Computation