Fast and accurate climate simulations and weather predictions are critical for understanding and preparing for the impact of climate change. Real-world weather and climate modeling consist of complex compound stencil kernels that do not perform well on conventional architectures. Horizontal diffusion is one such important compound stencil found in many climate and weather prediction models. Recent works propose using FPGAs as an alternative to traditional CPU and GPU-based systems to accelerate compound stencil kernels. However, we observe that compound stencil computations 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 horizontal diffusion stencil by designing the first scaled-out spatial accelerator using MLIR (Multi-Level Intermediate Representation) compiler framework. We evaluate its performance on a real cutting-edge AMD-Xilinx Versal AI Engine spatial architecture. Our real-system evaluation results demonstrate that SPARTA outperforms the state-of-the-art CPU, GPU, and FPGA implementations by 17.1x, 1.2x, and 2.1x, respectively. 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.

翻译：快速准确的气候模拟与天气预测，对于理解并应对气候变化的影响至关重要。现实中的天气与气候建模包含复杂的复合模板核函数，这类计算在传统架构上无法高效运行。水平扩散是许多气候与天气预报模型中的一类重要复合模板。近年来，研究者提出采用FPGA作为传统CPU和GPU系统的替代方案来加速复合模板核函数。然而，我们观察到复合模板计算因其复杂的内存访问模式无法利用FPGA提供的位级灵活性，导致硬件资源利用率高且峰值性能低。为此，我们提出SPARTA——一种针对水平扩散天气模板计算的新型空间加速器。通过利用二维空间架构，我们基于MLIR（多层次中间表示）编译器框架设计了首个可扩展空间加速器，从而高效加速水平扩散模板计算。我们在真实的尖端AMD-Xilinx Versal AI Engine空间架构上评估其性能。实测系统评估结果表明，SPARTA相比当前最先进的CPU、GPU及FPGA实现，性能分别提升17.1倍、1.2倍和2.1倍。研究结果揭示：在空间架构上均衡分配可用处理资源的负载，是实现高性能的关键。此外，我们还实现并评估了五种常用作模板计算研究基准的基础模板。为促进未来模板计算与空间计算系统的研究，我们在https://github.com/CMU-SAFARI/SPARTA 开源了所有实现代码。