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

Abstract

Fast and accurate climate simulations and weather predictions are critical for understanding and preparing for the impact of climate change. Real-world climate and weather simulations involve the use of complex compound stencil kernels, which are composed of a combination of different stencils. Horizontal diffusion is one such important compound stencil found in many climate and weather prediction models. Its computation involves a large amount of data access and manipulation that leads to two main issues on current computing systems. First, such compound stencils have high memory bandwidth demands as they require large amounts of data access. Second, compound stencils have complex data access patterns and poor data locality, as the memory access pattern is typically irregular with low arithmetic intensity. As a result, state-of-the-art CPU and GPU implementations suffer from limited performance and high energy consumption. Recent works propose using FPGAs as an alternative to traditional CPU and GPU-based systems to accelerate weather stencil kernels. However, we observe that stencil computation cannot leverage the bit-level flexibility available on an FPGA because of its complex memory access patterns, leading to high hardware resource utilization and low peak performance. We introduce SPARTA, a novel spatial accelerator for horizontal diffusion weather stencil computation. We exploit the two-dimensional spatial architecture to efficiently accelerate the horizontal diffusion stencil by designing the first scaled-out spatial accelerator using the MLIR (Multi-Level Intermediate Representation) compiler framework. We evaluate SPARTA on a real cutting-edge AMD-Xilinx Versal AI Engine (AIE) spatial architecture. Our real-system evaluation results demonstrate that SPARTA outperforms state-of-the-art CPU, GPU, and FPGA implementations by 17.1×, 1.2×, and 2.1×, respectively. Compared to the most energy-efficient design on an HBM-based FPGA, SPARTA provides 2.43× higher energy efficiency. Our results reveal that balancing workload across the available processing resources is crucial in achieving high performance on spatial architectures. We also implement and evaluate five elementary stencils that are commonly used as benchmarks for stencil computation research. We freely open-source all our implementations to aid future research in stencil computation and spatial computing systems at https://github.com/CMU-SAFARI/SPARTA.